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This article presents an algorithm for modelling an Adaptive Neuro Fuzzy Inference System (ANFIS) for power transformer paper conditions in order to estimate the transformer’s expected life. The dielectric characteristics, dissolved gasses, and furfural of 108 running transformers were collected, which were divided into 76 training datasets and another 32 testing datasets. The degree of polymerization (DP) of the transformer paper was predicted using the ANFIS model based on using the dielectric characteristics and dissolved gases as input. These inputs were analyzed, and the best combination was selected, whereas CO + CO2, acidity, interfacial tension, and color were correlated with the paper’s deterioration condition and were chosen as the input variables. The best combination of input variables and membership function was selected to build the optimal ANFIS model, which was then compared and evaluated. The proposed ANFIS model has 89.07% training accuracy and 85.75% testing accuracy and was applied to a transformer paper insulation assessment and an estimation of the expected life of four Indonesian transformers for which furfural data is unavailable. This proposed algorithm can be used as a furfural alternative for the general assessment of transformer paper conditions and the estimation of expected life and provides a helpful assistance for experts in transformer condition assessment.

This article presents an algorithm for modelling an Adaptive Neuro Fuzzy Inference System (ANFIS) for power transformer paper conditions in order to estimate the transformer's expected life. The dielectric characteristics, dissolved gasses, and furfural of 108 running transformers were collected, which were divided into 76 training datasets and another 32 testing datasets. The degree of polymerization (DP) of the transformer paper was predicted using the ANFIS model based on using the dielectric characteristics and dissolved gases as input. These inputs were analyzed, and the best combination was selected, whereas CO + CO2, acidity, interfacial tension, and color were correlated with the paper's deterioration condition and were chosen as the input variables. The best combination of input variables and membership function was selected to build the optimal ANFIS model, which was then compared and evaluated. The proposed ANFIS model has 89.07% training accuracy and 85.75% testing accuracy and was applied to a transformer paper insulation assessment and an estimation of the expected life of four Indonesian transformers for which furfural data is unavailable. This proposed algorithm can be used as a furfural alternative for the general assessment of transformer paper conditions and the estimation of expected life and provides a helpful assistance for experts in transformer condition assessment. [ABSTRACT FROM AUTHOR]

The digitization of distribution power systems has revolutionized the way data are collected and analyzed. In this paper, the critical task of harnessing this information to identify irregularities and anomalies in electricity consumption is tackled. The focus is on detecting non-technical losses (NTLs) and energy theft within distribution networks. A comprehensive overview of the methodologies employed to uncover NTLs and energy theft is presented, leveraging measurements of electricity consumption. The most common scenarios and prevalent cases of anomalies and theft among consumers are identified. Additionally, statistical indicators tailored to specific anomalies are proposed. In this research paper, the practical implementation of numerous artificial intelligence (AI) algorithms, including the artificial neural network (ANN), ANFIS, autoencoder neural network, and K-mean clustering, is highlighted. These algorithms play a central role in our research, and our primary objective is to showcase their effectiveness in identifying NTLs. Real-world data sourced directly from distribution networks are utilized. Additionally, we carefully assess how well statistical methods work and compare them to AI techniques by testing them with real data. The artificial neural network (ANN) accurately identifies various consumer types, exhibiting a frequency error of 7.62%. In contrast, the K-means algorithm shows a slightly higher frequency error of 9.26%, while the adaptive neuro-fuzzy inference system (ANFIS) fails to detect the initial anomaly type, resulting in a frequency error of 11.11%. Our research suggests that AI can make finding irregularities in electricity consumption even more effective. This approach, especially when using data from smart meters, can help us discover problems and safeguard distribution networks. [ABSTRACT FROM AUTHOR]

This paper presents a model for determining the availability of continuous systems at open pits using the neuro-fuzzy system. The concept of availability is divided into partial indicators (synthetic indicators and sub-indicators). The presented model in relation to already existing models for determining availability uses a combination of the advantages of artificial neural networks and fuzzy logic. The case study addressed the I ECC (bucket wheel excavator–conveyors–crushing plant) system of the open pit Drmno, Kostolac. In this paper, in addition to the ANFIS model for determining the availability of continuous systems, a simulation model was developed. The obtained results of the ANFIS model were verified with the help of a simulation model that uses certain assumptions about the distribution of failures. This paper was created as a result of several years of field and theoretical research into the availability of continuous systems in open pits, and completes a cycle that consists of several published articles on the subject of modeling the behavior of these systems in real time using a time picture of the state, expert assessment, simulation and AI models, while respecting the multidisciplinarity of the problem (mining technological, mechanical, and information technological aspects). The developed ANFIS model is a key instrument for improving operational efficiency and resource management in the mining sector. Its ability to accurately predict the availability of the ECC system brings not only operational benefits through reduced downtime and optimized maintenance, but also a potential reduction in overall costs at coal open pits. Such an innovative model marks a significant step forward in the mining industry, especially when it comes to continuous systems in coal open pits. [ABSTRACT FROM AUTHOR]

This paper presents a model for determining fuzzy evaluations of partial indicators of the availability of continuous systems at coal open pits using a neuro-fuzzy inference system. The system itself is a combination of fuzzy logic and artificial neural networks. The system availability is divided into partial indicators. By combining the fuzzy logic and artificial neural networks, a model is obtained that has the ability to learn and uses expert judgment for that learning. This paper deals with the ECC system (bucket wheel excavator-conveyor-crushing plant) of the open pit Drmno-Kostolac, which operates within the Electric Power Company of Serbia. The advantage of a model of this type is that it does not rely on the historical experiences of experts and usual predicted values for the fuzzy evaluation of partial indicators, which are based on the assumption that similar systems affect availability in a similar way. The fuzzy evaluation of partial indicators is based on historical data for the specific system for which the model was created. As such, it can more accurately predict continuous systems availability on the basis of expert evaluations in the appropriate time period. Another advantage of this model is that the availability is estimated on a quarterly basis, which gives a more accurate view because it uses a smaller time period with more similar characteristics and, thus, includes certain external influences which are related to the quarterly meteorological conditions. [ABSTRACT FROM AUTHOR]

Higher accuracy and meticulousness are highly demandable in modern industrial field during micro-machining performances by electrochemical discharge machining (ECDM) process. The paper deals with the experimental fuzzy logic control (FLC) analysis as well as artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) analysis during micro-channel fabrication on silica glass. A comparative analysis of FLC, ANN as well as ANFIS has been performed and experimental error prediction has been propounded to estimate minimum error possibilities such as mean absolute error (MAE), root mean square error (RMSE) and regression value (R). Computational time training dataset, minimum sample size and prediction time are also illustrated in this paper. In this paper, ANN, FLC and ANFIS models are analyzed for tool wear rate (TWR) and heat-affected zone (HAZ). 3D Rule Viewer and regression analysis as well as validation of test results and characteristics graph between performances with number of epochs of ANN model also are included in this paper for TWR and HAZ. Influence of process parameters like voltage, duty ratio, pulse frequency and electrolyte concentration on Surface Viewer of TWR and HAZ is also illustrated. It is found that ANFIS has great effectiveness of prediction of error during micro-ECDM process. [ABSTRACT FROM AUTHOR]

In view of the low accuracy of the motion parameters generated by the typical ship trajectory generator, and the fact that the problem of wind, current and wave interference is not considered, this paper establishes a new ship trajectory generator by analyzing the changes in the ship's attitude and speed under different motion states. Through simulation, the accuracy of the main motion parameters is significantly improved compared with the typical trajectory generator; the time-varying non-uniform wind, current and wave fields are constructed, and the interference effect of wind, current and waves on ship motion is analyzed by combining the empirical formulas of force and moment; an adaptive neuro fuzzy inference system (ANFIS) based on wind, current and wave interference is designed, and the fuzzy rules of the fuzzy system are determined by training and testing the measured data; the motion parameters of superimposed wind, current and wave interference are compared with the measured data, and the accuracy is further improved after superimposing wind, current and wave interference. [ABSTRACT FROM AUTHOR]

This paper presents the design of a novel trajectory tracking control strategy and the development of a mathematical model for a non-holonomic differential-drive wheeled mobile robot. The proposed control system utilizes a dual-loop approach, where the inner loop controls the dynamics by employing Adaptive Fuzzy Sliding Mode Control (AFSMC), and the outer loop, handles kinematics by utilizing an Adaptive Neuro-Fuzzy Inference System ;(ANFIS). The ANFIS is employed to minimize the error between the actual and desired velocities, providing a desired input for the inner loop. Meanwhile, the AFSMC is used to effectively control the system dynamics. The use of these dual-loop controllers considerably improves the system's overall efficiency. The inner controller compensates for dynamic disturbances, while the outer controller manages velocity errors. We integrate the actuator dynamics and the chopper effect of the wheels in the dynamics modeling, which helps to increase the models accuracy. MATLAB was used to implement the controller, while circular and eight-shaped trajectories were generated to assess the performance of the proposed controller. In addition, a comparative analysis of different controllers such as PID, SMC, AFSMC, and AFSMC with ANFIS was presented. The simulations were conducted under uncertainties, and the proposed controller is better than other controllers at tracking desired trajectories. The Lyapunov stability analysis is employed to verify the stability of the proposed controller. This paper shows that the proposed dual-loop controller is stable and more robust to internal parameter variation and external disturbance for the examined system. In general, the AFSMC with ANFIS is superior in trajectory tracking for the examined system compared to other controllers. [ABSTRACT FROM AUTHOR]

To improve the monitoring of the electrical power grid, it is necessary to evaluate the influence of contamination in relation to leakage current and its progression to a disruptive discharge. In this paper, insulators were tested in a saline chamber to simulate the increase of salt contamination on their surface. From the time series forecasting of the leakage current, it is possible to evaluate the development of the fault before a flashover occurs. In this paper, for a complete evaluation, the long short-term memory (LSTM), group method of data handling (GMDH), adaptive neuro-fuzzy inference system (ANFIS), bootstrap aggregation (bagging), sequential learning (boosting), random subspace, and stacked generalization (stacking) ensemble learning models are analyzed. From the results of the best structure of the models, the hyperparameters are evaluated and the wavelet transform is used to obtain an enhanced model. The contribution of this paper is related to the improvement of well-established models using the wavelet transform, thus obtaining hybrid models that can be used for several applications. The results showed that using the wavelet transform leads to an improvement in all the used models, especially the wavelet ANFIS model, which had a mean RMSE of 1.58 × 10 − 3 , being the model that had the best result. Furthermore, the results for the standard deviation were 2.18 × 10 − 19 , showing that the model is stable and robust for the application under study. Future work can be performed using other components of the distribution power grid susceptible to contamination because they are installed outdoors. [ABSTRACT FROM AUTHOR]

Predicting diseases in the early stages is extremely important. By taking advantage of advances in deep learning and fuzzy logic techniques, a new model is proposed in this paper for disease evaluation depending on the adaptive neuro-fuzzy inference system (ANFIS) with a genetic algorithm (GA) for classification, and the pre-trained DenseNet-201 model for feature extraction, in addition to the whale optimization algorithm (WOA) for feature selection. Two medical databases (chest X-ray and MRI brain tumor) for the diagnosis of two disease types were used as input in the suggested model. The optimization of ANFIS parameters was performed by GA to achieve the optimum prediction capability. DenseNet-201 for feature extraction was employed to obtain better classification accuracy. Having more features sometimes leads to lower accuracy, and this issue can be rectified using a feature selection strategy WOA which gave good results. The proposed model was evaluated utilizing statistical metrics root mean square error (RMSE), mean square error (MSE), standard deviation (STD), and coefficient of determination ( R 2 ), and it was compared with the conventional ANFIS model, with the proposed model (ANFIS-GA) showing a superior prediction capability over the ANFIS model. As a result, it can be concluded that the proposed ANFIS-GA model is efficient and has the potential for a robust diseases evaluation with good accuracy. Also, we conclude from this work that integrating optimization algorithms with ANFIS boosts its performance, resulting in a more accurate and reliable model. [ABSTRACT FROM AUTHOR]

This paper proposes a data-driven human-like driver model (HDM) based on the analysis and understanding of human drivers' behavior in path-tracking tasks. The proposed model contains a visual perception module and a decision-making module. The visual perception module was established to extract the visual inputs, including road information and vehicle motion states, which can be perceived by human drivers. The extracted inputs utilized for lateral steering decisions can reflect specific driving skills exhibited by human drivers like compensation control, preview behavior, and anticipation ability. On this basis, an adaptive neuro-fuzzy inference system (ANFIS) was adopted to design the decision-making module. The inputs of the ANFIS include the vehicle speed, lateral deviation in the near zone, and heading angle error in the far zone. The output is the steering wheel angle. ANFIS can mimic the fuzzy reasoning characteristics of human driving behavior. Next, a large amount of human driving data was collected through driving simulator experiments. Based on the data, the HDM was established. Finally, the results of the joint simulation under PreScan/MATLAB verified the superior performances of the proposed HDM. [ABSTRACT FROM AUTHOR]

The main aim of the research work presented in this paper consists of proposing an effective control scheme for a grid-connected single-phase photovoltaic (PV) system to enhance not only the power quality at the point of common coupling (PCC) but also to operate with a maximum power point tracking (MPPT) controller. Moreover, an orthogonal signal generator (OSG) module for effective grid synchronization, a current reference generation controller, and a PWM generating block have also been designed and included in this paper. The proposed control strategy allows the MPPT controller to switch to faulty mode and maintains the voltage according to network requirements using an adaptive neuro-fuzzy inference system (ANFIS)-based control whenever a fault occurs at the PCC. The performance of the analyzed control strategy, which is based on the static compensation of the DC-link voltage fluctuations in a grid-connected inverter powered by PV, is further explored through simulations in MATLAB, and the results are included in this paper. Moreover, the control scheme is implemented experimentally using a dSPACE DS 1104 control board and then assessed on a small laboratory-scale single-phase PV system that is subjected to some fault scenarios. The simulation and experimental results have shown improved power quality and robustness against grid fluctuations, resulting in better dynamic performance. [ABSTRACT FROM AUTHOR]

Matlab is a useful tool in many fields of science and technology, especially control theory. In this paper, toolkits of Matlab (Anfis and Neuron Network Training tool) are used to learn the operation of Incremental Sliding Mode Control algorithm in trajectory tracking control for Rotary Inverted Pendulum. The data learning is collected from experiment and verified through real-time control. The results in this paper show the ability of these two toolkits on learning control process. Thence, this method can be extended to apply on other control objects. [ABSTRACT FROM AUTHOR]

Nowadays, grid power loss and power quality deterioration are causing many problems, especially for sensitive uses such as data centers, airports, healthcare, banking, insurance, and telecom. These issues must be resolved through energy continuity coverage and optimization. Dynamic voltage restorers (DVR) are generally used to mitigate network voltage disturbances and maintain a constant voltage value between load terminals for a short and limited duration due to limited energy storage facilities. However, this paper's proposed (DVR) system controlled by the adaptive-network fuzzy inference system (ANFIS) controller can compensate for prolonged power quality disturbances by integrating a hybrid renewable energy system (HRES) managed using classical Proportional Integral (PI) controller power management. This paper proposes a new (DVR) topology coupled with a hybrid renewable energy system (HRES) to exploit free and clean energy, consisting of a solar panel, a PEM fuel cell, and a battery storage device connected through DC-DC converters to a DC transmission, the obtained results from the simulation process of the proposed DVR system in the environment of MATLAB / Simulink showed the ability to eliminate sag that exceeded 0.9 pu in a period of more than 3 min and swells that exceeded 1.2 pu in a period that exceeded one minute. In comparison, the load voltage's total harmonic distortion THDv was reduced from 29% to 5%, and the source current total harmonic distortion THDi from 30.25% to 2.79%. [ABSTRACT FROM AUTHOR]

Nowadays, spoken digit recognition is widely used for various applications. It is a technique used in automatic speech recognition to identify and classify the language spoken by a particular speaker. Several techniques and methods for spoken digit recognition have been proposed. Most of the method includes artificial neural networks, Gaussian mixture models and neuro-fuzzy approach. However, there needs to be a more efficient algorithm that gives more satisfactory and better results. Therefore, to enhance the results in terms of accuracy and running time, a hybrid adaptive neuro-fuzzy approach called KANFIS-SCG is proposed in this paper. This approach combines the Adaptive Network-Based Fuzzy Inference System (ANFIS) and a fast supervised learning algorithm called the Scaled Conjugate Gradient (SCG). In addition, the K-means clustering algorithm is used to reduce the number of fuzzy inference rules. The features are extracted from speech samples using the Mel Frequency Cepstral Coefficient (MFCC) algorithm. Furthermore, the covariance matrix provides more precise information about the features. The experiment was carried out in two languages, namely Hindi and English, and the database was prepared for both languages where utterances were digits from zero to nine. The proposed method KANFIS-SCG gives average recognition rates of 99.12% and 98.54% for Hindi and English, respectively, for the speaker-dependent system, which is better than the existing ANFIS method. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of a road-type-adaptive control strategy aimed at enhancing suspension performance. H 2 synthesis is employed for modeling road irregularities as impulses or white noise, minimizing the root mean square (RMS) of performance outputs for these specific road types. It should be noted, however, that this approach may lead to suboptimal performance when applied to other road profiles. In contrast, the H ∞ controller is employed to minimize the RMS of performance outputs under worst-case road irregularities, taking a conservative stance that ensures robustness across all road profiles. To leverage the advantages of both controllers and achieve overall improved suspension performance, automatic switching between these controllers is recommended based on the identified road type. To implement this adaptive switching mechanism, manual switching is performed, gathering input–output data from the controllers. These data are subsequently employed for training an Adaptive Neuro-Fuzzy Inference System (ANFIS) network. This elegant approach contributes significantly to the optimization of suspension performance. The simulation results employing this novel ANFIS-based controller demonstrate substantial performance enhancements compared to both the H 2 and H ∞ controllers. Notably, the ANFIS-based controller exhibits a remarkable 62% improvement in vehicle body comfort and a significant 57% enhancement in ride safety compared to passive suspension, highlighting its potential for superior suspension performance across diverse road conditions. [ABSTRACT FROM AUTHOR]

As a part of the food industry, the dairy industry is one of the most important sectors of the process industry, keeping in mind the number of employees in that sector, the share in the total industrial production, and the overall value added. Many strategies have been developed over time to satisfy customer needs and assess customer satisfaction. This paper proposes an innovative model based on adaptive neuro-fuzzy inference system (ANFIS) and elements of the ACSI (American customer satisfaction index) for assessing and monitoring the level of customer satisfaction in a dairy manufacturing company where there are no large seasonal variations. In terms of an innovative approach, the base of fuzzy logic rules is determined by applying the fuzzy Delphi technique for the application of the ANFIS algorithm and assessment of customer satisfaction. The verification of the model is delivered by testing a real sample from a company of the dairy industry. As decisions on the strategic company level may be impacted by customer satisfaction, the company management should choose the most precise methodology for customer satisfaction assessment. The results are compared with other methods in terms of mean absolute deviation (MAD), mean squared error (MSE), and mean absolute percentage error (MAPE). Results show that ANFIS outperformed other methods used for assessing the level of customer satisfaction, such as case-based reasoning and multiple linear regression. [ABSTRACT FROM AUTHOR]

Exoskeletons are powered robotic devices designed to be worn by humans to provide physical assistance or power augmentation. In this work, a control system for a powered exoskeleton is designed. This exoskeleton is aimed at aiding in the rehabilitation of Spinal Bifidas. Spinal Bifida is the most common disability in childhood after Cerebral Palsy, it is a defective development of the spinal cord during conception. Two phases for this work are presented: system identification and control using ANFIS. While it is difficult to attain an accurate dynamical model of complex system, this work employed ANFIS to help control and stabilize the system. Gait trajectories were obtained by modeling the system as a linear inverted pendulum, a simulation was performed with a traditional controller. Afterwards, trajectory data was obtained and used to train and test ANFIS to create the model and controller. One, two and three inputs were investigated to train the ANFIS. Results showed that the one-input model visibly failed to follow the trajectory. The average RMSE for the two-input model was 0.096, and for the three-inputs, the RMSE on average was higher; 0.19, making it worse, however the knee model contrastingly showed improvement, as the RMSE was lower by 2% for the knee specifically. [ABSTRACT FROM AUTHOR]

Environment and climate change have caused a rise in a wide range of diseases and infections. In countries where overpopulation is a problem, many infections spread severely. The main focus of this paper is the detection and identification of blood diseases. An automated system that examines all potential diseases using patient information and data is needed to deal with unpredictable circumstances. Having an automated and intelligent system that evaluates the reports and counsels doctors in any other area or nation is a demand of the time. The same solutions can be identified by the proposed system. To apply the adaptive neuro-fuzzy inference system (ANFIS) and related techniques to predict chronic diseases early, the authors have gone through various existing models and case studies on diabetics and other patients. The proposed approach, called S-ANFIS which is using the hybrid approach, is based on ANFIS and includes content curation and intelligence analysis in addition to comparison with current models. As a result, the suggested model outperforms other approaches in terms of disease prediction accuracy, with a score of 88.6%. [ABSTRACT FROM AUTHOR]

Applying machine learning (ML) and fuzzy inference systems (FIS) requires large datasets to obtain more accurate predictions. However, in the cases of oil spills on ground environments, only small datasets are available. Therefore, this research aims to assess the suitability of ML techniques and FIS for the prediction of the consequences of oil spills on ground environments using small datasets. Consequently, we present a hybrid approach for assessing the suitability of ML (Linear Regression, Decision Trees, Support Vector Regression, Ensembles, and Gaussian Process Regression) and the adaptive neural fuzzy inference system (ANFIS) for predicting the consequences of oil spills with a small dataset. This paper proposes enlarging the initial small dataset of an oil spill on a ground environment by using the synthetic data generated by applying a mathematical model. ML techniques and ANFIS were tested with the same generated synthetic datasets to assess the proposed approach. The proposed ANFIS-based approach shows significant performance and sufficient efficiency for predicting the consequences of oil spills on ground environments with a smaller dataset than the applied ML techniques. The main finding of this paper indicates that FIS is suitable for prediction with a small dataset and provides sufficiently accurate prediction results. [ABSTRACT FROM AUTHOR]

This paper presents a design and real-time application of an efficient Artificial Intelligence (AI) method assembled with PID controller of an eddy current dynamometer (ECD) for robustness due to highly nonlinear system by reason of some magnetism phenomena such as skin effect and dissipated heat of eddy currents. PID Control which is known as the most popular conventional control method in industry is inadequate for such nonlinear systems. On the other hand, Adaptive Neural Fuzzy Interference System (ANFIS), Single Hidden Layer Neural Network (SHLNN), General Regression Neural Network (GRNN), and Radial Basis Neural Network (RBNN) are examples used as artificial intelligence-based techniques that can increase the performance of conventional control systems in particular. The proposed control system proves changeable Kp (Proportional gain), Ki, (Integral gain) and Kd (Derivative gain) parameters in real-time to adapt and presents a good capacity to adapt nonlinearities and bring robustness using 4 different versatile soft computing methods of ANFIS, SHLNN, GRNN, and RBNN. The testing dataset is extracted from experimental studies and its robustness has also been verified with different Artificial Intelligence (AI) methods. The presented technique is observed to have a good performance in terms of response time (t) and accuracy of desired speed value (V) under different parameters such as non-linear dynamics (V, T) of the system elements and the varying load effects. [ABSTRACT FROM AUTHOR]

To improve the automation level of the welding robots, the automatic extraction and identification of the weld seam is the pre-requisite of the intelligent welding robots. During the real industrial environment, the welding environment often has the characteristics of weak texture, poor contrast, reflections from metallic surfaces, and imperfections on the work piece. These characteristics will seriously affect the accurate extraction of weld seam. To realize the automatic path planning of welding robots, a new method of automatic extraction and identification of narrow butt joint under weak contrast is realized to serve the 3D path teaching of welding robots. To achieve accurate extraction of narrow butt joint under weak contrast, this paper designs a new seam extraction operator to achieve accurate and fast extraction of weld seam with various shapes and sizes. And the shape feature of weld seam is used to construct feature vector. Finally, to get optimal classification performance, the adaptive network-based fuzzy inference system (ANFIS) is adopted in this paper to finish identification of narrow butt joints. The experimental results show that the proposed algorithm could quickly and accurately realize the extraction and identification of the narrow butt joints under weak contrast. [ABSTRACT FROM AUTHOR]

The ride comfort and safety of passenger rail vehicles depend on the performance of the suspension system in attenuating vibrations induced by track irregularities. This paper investigates the effectiveness of an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based semi-active controlled suspension system using a magnetorheological fluid damper in reducing nonlinear lateral vibrations of a passenger rail vehicle. A complete rail vehicle model is developed, including the carbody, front and rear bogies, and the passive suspension system's nonlinear stiffness and damping characteristics are considered from experimental data. The passive suspension model is validated through experiments, and an ANFIS-based controller is incorporated with the secondary vertical suspension system to improve ride behavior. Three semi-active suspension strategies are considered under varying speeds and track irregularities, and their effectiveness is compared to the nonlinear passive suspension system in terms of rms acceleration, rms displacement, ride quality, and comfort. The results shows that the ANFIS-based semi-active suspension system with a magnetorheological fluid damper outperforms the passive suspension system and semi-active strategies in all tested conditions. There is a reduction in rms acceleration by approximately 11.11% to 23.64% and rms displacement by about 5.36% to 32.06%. Moreover, it significantly improves ride quality (9.20% to 31.02%) and comfort (9.96% to 31.50%). The rms acceleration and displacement are reduced, and the Sperling ride index and Percentage Reduction Index values demonstrate that the ANFIS-based semi-active suspension effectively minimizes the impact of rail irregularities and vibrations, resulting in a significant gain in ride quality and passenger comfort. [ABSTRACT FROM AUTHOR]

Electrical discharge machining (EDM) is a widely used non-conventional machining technique in manufacturing industries, capable of accurately machining electrically conductive materials of any hardness and strength. However, to achieve low production costs and minimal machining time, a comprehensive understanding of the EDM system is necessary. Due to the stochastic nature of the process and the numerous variables involved, it can be challenging to develop an analytical model of EDM through theoretical and numerical simulations alone. This paper conducts an extensive review of the various experimental (or empirical) modeling techniques used by researchers over the past two decades, including a geographic and temporal analysis of these approaches. The major methods employed to describe the EDM process include regression, response surface methodology (RSM), fuzzy inference systems (FIS), artificial neural networks (ANN), and adaptive neuro-fuzzy inference systems (ANFIS). Additionally, the optimization methods used in conjunction with these methods are also discussed. Although RSM is the most commonly used empirical modeling technique, recent years have seen an increase in the use of ANN for providing the most accurate predictions of EDM process responses. The review of the literature shows that most of the investigations on experimental EDM modeling were conducted in Asia. [ABSTRACT FROM AUTHOR]

With recent advancements in machine-to-machine (M2M), the demand for fastest communication is an utmost concern of the M2M technology. The advent of 5G telecommunication networks enables to bridge the demand on satisfying the Quality-of-Service (QoS) concerns in M2M communication. The massive number of devices in M2M communication is henceforth do not lie under limited resource allocation by embedding the 5G telecommunication network. In this paper, we address the above limitation of allocation the resource to prominent M2M devices using Adaptive Neuro Fuzzy Inference System (ANFIS). In ANFIS, the adoption of rules will imply the resource allocation with the devices of top priority and it reduces based on the priority. The ANFIS controller acts as a central controller that implies the resource allocation with its rules on the M2M devices. The simulation is performed to test the efficacy of fuzzy logic system on allocation 5G resources to M2M model. The results show that the ANFIS model achieves higher level of allocating the resources than other existing methods in terms of reduced network delay, increased throughput, packet delivery rate and energy efficiency. [ABSTRACT FROM AUTHOR]

Featured Application: The results of the work may find potential application in the final quality control of parts on a production line. This paper deals with the development of dimensional control technology for the production of accessory drive train (ADT) gearbox housing, according to the closed door technology approach. The work presents the methodology of the final inspection of bearing seat position deviation by replacing the coordinate measuring machines (CMMs) with a computerized numerical control (CNC) machine and adaptive neuro-fuzzy inference system. The results of the work indicated that correct solutions were obtained. In addition, the technological process of manufacturing is fully automated and performed entirely on the production line. [ABSTRACT FROM AUTHOR]

The Electrical Power System (EPS) in an aircraft is designed to interact extensively with other systems. With a growing trend towards more electric aircraft, the complexity of interactions between the EPS and other systems has grown. This has resulted in an increased necessity of implementing health monitoring methods like diagnosis and prognosis of the EPS at the systems level. This paper focuses on developing a diagnostic algorithm for the EPS to detect and isolate faults and their root causes that occur at the Line Replaceable Units (LRUs) connecting with aircraft systems like the engine and the fuel system. This paper aims to achieve this in two steps: (i) developing an EPS digital twin and presenting the simulation results for both healthy and fault scenarios, (ii) developing an Adaptive Neuro-Fuzzy Inference System (ANFIS) monitor to detect faults in the EPS. The results from the ANFIS monitor are processed in two methods: (i) a crisp boundary approach, and (ii) a fuzzy boundary approach. The former approach has a poor misclassification rate; hence the latter method is chosen to combine with causal reasoning for isolating root causes of these interacting faults. The results from both these methods are presented through examples in this paper. [ABSTRACT FROM AUTHOR]

This paper presents an investigation of wire electrical discharge machining (WEDM) of new fabricated aluminium base composite with 5% graphite by weight, i.e., Al/Gr/Cp5. The new fabricated metal matrix composite (MMC) has a complete application in space, defence, automobile, and other concerned metal industries. In this work, adaptive neuro-fuzzy inference system (ANFIS) and the response surface method (RSM) were selected for the analysis and modelling purposes. ANFIS is a soft computing technique used in nonlinear data training. Taguchi's L27 plan was employed to conduct the experiments. The process parameters such as pulse-on time (PON), pulse-off time (POFF), a feed rate of wire (FRW) and the input current (IC) were selected for the experimentation. Based on the experimental findings, PON is identified as the most dominant parameter and the input current. WEDM performance was measured in terms of surface roughness (SR). For the optimised (minimum) surface roughness of 1.8625 microns, the best set of process parameters was PON (108 μs), POFF (52 μs), FRW (4 m/min) and the input current (12 Amp). ANFIS-predicted results were compared with the experimental results. An acceptable agreement is observed with the high value of the correlation coefficient (R2 = 0.9624). The soft computing technique, like ANFIS-RSM, shows a good competency and reliability for the presented work. [ABSTRACT FROM AUTHOR]

In this paper, the application of an Adaptive Network-based Fuzzy Inference System (ANFIS) to control a hybrid solar and wind grid-tie inverter in order to reduce power oscillations and enhance power quality is presented. To extract the maximum power from the PV system, a Perturb and Observe (P&O) algorithm is presented that tracks the Maximum Power Point (MPP). Time-domain simulation results of the studied system are performed in MATLAB/SIMULINK under different operating conditions such as changing irradiation and short-circuit faults in the power grid. From the simulation results, it can be concluded that the designed ANFIS controller and the proposed P&O algorithm perform better than the traditional PI controller and improve transient responses under severe operating conditions. [ABSTRACT FROM AUTHOR]

Wide acceptance of mobile phones and their resource hungry applications have highlighted resource limitations of mobile devices. In this regard, cloud computing has provided mobile phones with unlimited resources in order to help them overcome their constraints and enable them to support wider range of applications; so, mobile devices can outsource their tasks to public or local clouds. To accommodate to exponential growth of requests, user requests should be distributed to different cloudlets and then transparently and dynamically redirected to the servers according to the latest network and server status. Therefore, finding the best place to off-load is vital and crucial to both functionality and performance of the system. However, accurate and timely parameters of network and servers' status are improbable to achieve, so the traditional algorithms cannot perform effectively and fully efficient. As a solution in this paper, an adaptive neuro-fuzzy inference system is proposed and trained to assign tasks to the servers efficiently. The trained system is robust to imprecise context information and is tolerable measurement noise and errors. We have considered improving both system performance and user quality of service parameters in this paper. Simulation results demonstrate that, compared with other server selection schemes, the proposed scheme can achieve higher resource utilization (utilization is a percentage of time that a server is busy doing something), provide better user-perceived quality of service, and efficiently deal with network dynamics. Simulation results show that our proposed algorithm excels over the compared works in terms of performance, at the best case about 30% and at the worst case about 8.93%. [ABSTRACT FROM AUTHOR]

Most people are now aware of the importance of a healthy lifestyle, including the importance of consuming vegetables. As a result, the demand for vegetables has increased, and so their production needs to be increased. Currently, most plantations use soil as a growing medium, which is time-consuming and requires a significant amount of space. To modernize cultivation, hydroponic techniques should be adopted. However, implementing hydroponics can be challenging as it requires precise pH and nutrient adjustments. The previous research has proposed the hydroponic pH and nutrient control using the Sugeno fuzzy method. However, in Sugeno fuzzy method, there is no systematic procedure in designing the fuzzy controller, thus, the design relies on hydroponic expert knowledge. To address this issue, a smart hydroponic system was developed using the adaptive neuro-fuzzy inference system (ANFIS) method, which allows for automatic adjustments based on the collected dataset and remote control through internet of things (IoT) technology. This study showed that the system could accurately adjust pH and nutrient levels, allowing plants to grow better. Furthermore, the fuzzy controller created using ANFIS is 67% more accurate than creating the fuzzy controller using the Sugeno fuzzy method. Finally, the web application dashboard of the proposed system is also presented in this paper. [ABSTRACT FROM AUTHOR]

In the primary and final designs of projects related to rock mechanics and engineering geology, one of the key parameters that needs to be taken into account is the intact rock elastic modulus (E). To measure this parameter in a laboratory setting, core samples with high-quality and costly tools are required, which also makes for a time-consuming process. The aim of this study is to assess the effectiveness of two meta-heuristic-driven approaches to predicting E. The models proposed in this paper, which are based on integrated expert systems, hybridize the adaptive neuro-fuzzy inference system (ANFIS) with two optimization algorithms, i.e., the differential evolution (DE) and the firefly algorithm (FA). The performance quality of both ANFIS-DE and ANFIS-FA models was then evaluated by comparing them with ANFIS and neural network (NN) models. The ANFIS-DE and ANFIS-FA models were formed on the basis of the data collected from the Azad and Bakhtiari dam sites in Iran. After applying several statistical criteria, such as root mean square error (RMSE), the ANFIS-FA model was found superior to the ANFIS-DE, ANFIS, and NN models in terms of predicting the E value. Additionally, the sensitivity analysis results showed that the P-wave velocity further influenced E compared with the other independent variables. [ABSTRACT FROM AUTHOR]

The present paper represents an experimental and theoretical study of the characteristics of natural convection and radiation heat transfer from a solid cylindrical rod inside a closed vessel under vacuum conditions. The effect of vacuum pressure on the heat transfer rates from the heating element by convection and radiation is clarified. The experimental results are used for Artificial Neural Network (ANN) training which is employed in the Adaptive Neuro Fuzzy Inference System (ANFIS) scheme to predict the heat transfer characteristics. The heat transfer predictions are performed for the solid rod under numerous heat fluxes for each vacuum pressure and the computed outcomes are compared with that predicted from the ANFIS. Outcomes indicated that for a constant heat flux, the convection heat transfer decrease with the reduction in pressure inside the vessel, while the radiation heat transfer will increase. Three methods of ANFIS are deemed in this work; grid partition, subtractive clustering and fuzzy C-mean clustering (FCM). However, the three methods appear an adequate fitting with the experimental data due to the high ability of these schemes at formulating input-output relations. A comparison with conventional correlation for the Nusselt number (Nu) has been implemented which proved the effectiveness and robustness of artificial intelligence in the generation of a direct relation between the effective parameters and the objective functions. Also, the main factors of ANFIS such as the number of membership functions (MFs), the number of clusters and the radius of clusters have been optimized to reach best performance. [ABSTRACT FROM AUTHOR]

Inertial navigation system (INS) is often integrated with satellite navigation systems to achieve the required precision at high-speed applications. In global navigation system (GPS)/INS integration systems, GPS outages are unavoidable and a severe challenge. Moreover, because of the usage of low-cost microelectromechanical sensors (MEMS) with noisy outputs, the INS will get diverged during GPS outages, and that is why navigation precision severely decreases in commercial applications. In this paper, we improve GPS/INS integration system during GPS outages using extended Kalman filter (EKF) and artificial intelligence (AI) together. In this integration algorithm, the AI receives the angular rates and specific forces from the inertial measurement unit (IMU) and velocity from the INS at t and t − 1. Therefore, the AI has positioning and timing data of the INS. While the GPS signals are available, the output of the AI is compared with the GPS increment; so that the AI is trained. During GPS outages, the AI will practically play the GPS role. Thus, it can prevent the divergence of the GPS/INS integration system in GPS-denied environments. Furthermore, we utilize neural networks (NNs) as an AI module in five different types: multi-layer perceptron (MLP) NN, radial basis function (RBF) NN, wavelet NN, support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS). To evaluate the proposed approach, we utilize a real dataset that has been gathered by a mini-airplane. The results demonstrate that the proposed approach outperforms the INS and GPS/INS integration systems with the EKF during GPS outages. Meanwhile, the ANFIS also reached more than 47.77% precision compared to the traditional method. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach for solar power forecasting using an Adaptive Neuro-Fuzzy Inference System (ANFIS) enhanced with Whale Swarm Optimization (WSO). The synergy between ANFIS and WSO aims to overcome the limitations of traditional forecasting models by controlling the collective intelligence of a whale-inspired swarm algorithm. The WSO optimizes the parameters of the ANFIS, leading to improved accuracy in solar power predictions. The integration of WSO introduces a parallelism and exploration-exploitation balance inspired by the natural behaviors of whales, enhancing the ANFIS model's adaptability to dynamic solar power generation patterns. The experimental results demonstrate the superiority of the proposed Whale Swarm-based ANFIS over conventional methods, showcasing its ability to handle non-linear and complex relationships in solar power data. This research contributes to the field of renewable energy forecasting by presenting an innovative hybrid model that leverages the strengths of both ANFIS and WSO for more reliable and precise solar power predictions. [ABSTRACT FROM AUTHOR]

Oil production estimation plays a critical role in economic plans for local governments and organizations. Therefore, many studies applied different Artificial Intelligence (AI) based methods to estimate oil production in different countries. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is a well-known model that has been successfully employed in various applications, including time-series forecasting. However, the ANFIS model faces critical shortcomings in its parameters during the configuration process. From this point, this paper works to solve the drawbacks of the ANFIS by optimizing ANFIS parameters using a modified Aquila Optimizer (AO) with the Opposition-Based Learning (OBL) technique. The main idea of the developed model, AOOBL-ANFIS, is to enhance the search process of the AO and use the AOOBL to boost the performance of the ANFIS. The proposed model is evaluated using real-world oil production datasets collected from different oilfields using several performance metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), coefficient of determination (R2), Standard Deviation (Std), and computational time. Moreover, the AOOBL-ANFIS model is compared to several modified ANFIS models include Particle Swarm Optimization (PSO)-ANFIS, Grey Wolf Optimizer (GWO)-ANFIS, Sine Cosine Algorithm (SCA)-ANFIS, Slime Mold Algorithm (SMA)-ANFIS, and Genetic Algorithm (GA)-ANFIS, respectively. Additionally, it is compared to well-known time series forecasting methods, namely, Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Seasonal Autoregressive Integrated Moving Average (SARIMA), and Neural Network (NN). The outcomes verified the high performance of the AOOBL-ANFIS, which outperformed the classic ANFIS model and the compared models. [ABSTRACT FROM AUTHOR]

The paper presents the prediction of surface quality during the electro-discharge machining (EDM) of aluminium-based alloy. The composition consists of Aluminium, copper, nickel (Al/Cu/Ni) alloy. The techniques like dimensional exponential model (DEM), response surface-based modelling (RSM), and adaptive neuro-fuzzy inference based system (ANFIS) were employed to analyse the EDM process. Response variable i.e. Surface characteristic measure in terms of roughness (Ra) is the dependent parameter. Experimental analysis was done using Taguchi's L18 mixed OA's i.e. orthogonal-array by considering the process parameters namely variation in the material composition (CP), pulse-on-time(TON), pulse-off- time(TOFF) and input current(IP). Analysis of variances (ANOVA) has been employed out to identify the significant process parameters and their impact over surface characteristics. Models based on the DEM and RSM were developed to analyse the surface characteristics or roughness of the Al/Cu/Ni alloy. The experimental result proves the commitment of all three approaches to represent the process effectually. The correlation coefficient (R2) between the experimental and the DEM, RSM, and ANFIS predicted roughness was 0.830626, 0.995649, and 0.999958 respectively. This value shows the dominance of the ANFIS technique over the DEM and the RSM. [ABSTRACT FROM AUTHOR]