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In this paper, we shed light on the use of two types of fuzzy genetic algorithms, which stand out from the literature due to the innovative ideas behind them. One is the Gendered Fuzzy Genetic Algorithm, where the crossover mechanism is regulated by the gender and the age of the population to generate offspring through proper fuzzy rules. The other one is the Elegant Fuzzy Genetic Algorithm, where the priority of the parent genome is updated based on the child's fitness. Both algorithms present a significant computational burden. To speed up the computation, we propose to adopt a nearest-neighbor caching strategy. We first performed several experiments, using some well-known benchmark functions, and tried different types of membership functions and logical connectives. Afterward, some additional benchmarks were retrieved from the literature for a fair comparison against published results, which were obtained by means of former variants of fuzzy genetic algorithms. A real-world application problem, which was retrieved from the literature and dealt with rice production, was also tackled. All the numerical results show the potential of the proposed strategy. [ABSTRACT FROM AUTHOR]

Power dispatch is a real-time process that requires accurate decisions to be made in a short period of time. It is necessary to improve the calculation speed as much as possible while ensuring the accuracy of the calculation, in order to meet the real-time requirements. Therefore, a design method for error-proof adaptive optimization model of power dispatch under time redundancy control is proposed. Build an intelligent monitoring platform for the operation status of power dispatch equipment, and use this platform to achieve real-time analysis and processing of power grid operation data. Use state estimation method to obtain accurate state estimation results of the power system, use fuzzy logic method for error analysis, reduce the impact of errors, and improve the operational efficiency and stability of the power system. Finally, by controlling time redundancy, the stability of basic parameters such as voltage and frequency in the power system is ensured, achieving rapid start–stop of generator units and flexible load adjustment and improving the schedulability and economy of the power system. Experimental results show that the proposed method can effectively reduce the root mean square error and average absolute error of error prevention results at the power dispatching terminal and ensure the stability of power system operation, and the model calculation speed is fast, which verifies the effectiveness of the method. [ABSTRACT FROM AUTHOR]

The patterns of providing health-centric services have transformed extremely with the enhancement along with innovations in mobile and wireless communication technologies subsuming the Internet of Things (IoT). Due to the rapidly increasing attack, the doctors were not provided with an accurate alerting mechanism by the prevailing health monitoring system. Thus, by utilizing the Exponential Linear activation Units-centred Bidirectional Long Short Term Memory (ELUS-BiLSTM) technique, a novel healthcare monitoring along with an attack detection system is proposed in this work. Attack detection, Data security, and Patient health monitoring are the three primary phases incorporated in the proposed methodology. Initially, from the patient, the data are collected, and then the features are extracted in the attack detection phase. Next, the features being extracted are inputted to the ELUS-BiLSTM classifier where the data is classified as attacked or non-attacked data. After that, by utilizing Skew Tent Elliptic Curve Advanced Encryption Standard (STECAES), the non-attacked data is encrypted whereas the attacked data is stored in the log file. Lastly, to generate the fuzzy rules, the encrypted data is utilized; subsequently, the alert message is sent to the doctor. The experiential outcomes displayed that when analogized with the prevailing methodologies, the proposed model obtained better outcomes. [ABSTRACT FROM AUTHOR]

The classification of electroencephalogram (EEG) signals derived from motor imagery (MI) has always been a hot topic in the field of brain–computer interfaces. Due to its ability to handle the nonstationary and uncertain information contained in EEG signals, the Takagi–Sugeno–Kang fuzzy system (TSK-FS) has become an advantageous classification algorithm. To train a fuzzy system with strong discrimination capabilities from EEG data interspersed with redundant information, this paper proposes a TSK-FS modeling method based on low-rank sparse subspace learning (TSK-LSSL). This method focuses on consequent parameter learning, which transforms the traditional consequent parameter learning strategy into low-rank subspace and sparse subspace learning processes. Low-rank subspace learning is used to mine the global structural information of data and effectively reduce the number of fuzzy rules. During sparse subspace learning, ℓ 2 , 1 -norm regularization is used to constrain the consequent parameters and causes the number of redundant consequent parameters to be zero, thereby simplifying the fuzzy rules. In addition, a local boundary term based on graph matrices is embedded into the objective function to mine the local structural information of the given data. TSK-LSSL simplifies the number of rules and the consequent part of the fuzzy rules. It exhibits good classification performance on two BCI Competition databases. [ABSTRACT FROM AUTHOR]

The security of the wireless sensor network-Internet of Things (WSN-IoT) network is more challenging due to its randomness and self-organized nature. Intrusion detection is one of the key methodologies utilized to ensure the security of the network. Conventional intrusion detection mechanisms have issues such as higher misclassification rates, increased model complexity, insignificant feature extraction, increased training time, increased run time complexity, computation overhead, failure to identify new attacks, increased energy consumption, and a variety of other factors that limit the performance of the intrusion system model. In this research a security framework for WSN-IoT, through a deep learning technique is introduced using Modified Fuzzy-Adaptive DenseNet (MF_AdaDenseNet) and is benchmarked with datasets like NSL-KDD, UNSWNB15, CIDDS-001, Edge IIoT, Bot IoT. In this, the optimal feature selection using Capturing Dingo Optimization (CDO) is devised to acquire relevant features by removing redundant features. The proposed MF_AdaDenseNet intrusion detection model offers significant benefits by utilizing optimal feature selection with the CDO algorithm. This results in enhanced Detection Capacity with minimal computation complexity, as well as a reduction in False Alarm Rate (FAR) due to the consideration of classification error in the fitness estimation. As a result, the combined CDO-based feature selection and MF_AdaDenseNet intrusion detection mechanism outperform other state-of-the-art techniques, achieving maximal Detection Capacity, precision, recall, and F-Measure of 99.46%, 99.54%, 99.91%, and 99.68%, respectively, along with minimal FAR and Mean Absolute Error (MAE) of 0.9% and 0.11. [ABSTRACT FROM AUTHOR]

This article presents an investment recommender system based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) and pre-trained weights from a Multimodal Neural Network (MNN). The model is designed to support the investment process for the customers and takes into consideration seven factors to implement the proposed investment system model through the customer or potential investor data set. The system takes input from a web-based questionnaire that collects data on investors' preferences and investment goals. The data is then preprocessed and clustered using ETL tools, JMP, MATLAB, and Python. The ANFIS-based recommender system is designed with three inputs and one output and trained using a hybrid approach over three epochs with 188 data pairs and 18 fuzzy rules. The system's performance is evaluated using metrics such as RMSE, accuracy, precision, recall, and F1-score. The system is also designed to incorporate expert feedback and opinions from investors to customize and improve investment recommendations. The article concludes that the proposed ANFIS-based investment recommender system is effective and accurate in generating investment recommendations that meet investors' preferences and goals. [ABSTRACT FROM AUTHOR]

Abstract A radial basis function neural network PID controller under fuzzy rules (FUZZY-RBF-PID) was designed for the electro-hydraulic position servo system under the influence of uncertain factors such as load mutation, and load stiffness change. Firstly, the mathematical model of the system is established, and the frequency domain and time domain analysis of the system are carried out. Secondly, based on the analysis results, a radial basis function (RBF) neural network PID controller is designed, and fuzzy rules are innovatively used to adjust the learning rate of PID parameters in the RBF neural network learning algorithm in real time. Thirdly, the simulation results show that under the action of the FUZZY-RBF-PID controller, the unit step response of the system has high steady-state accuracy, fast response speed, and under the condition of large load stiffness, the system can recover to the steady-state value faster after being disturbed. At the same time, when the input signal is the sinusoidal signal of 10 HZ, the system under the action of the FUZZY-RBF-PID controller has no obvious phase lag phenomenon, and the tracking error is minimal. The proposed method can effectively improve the comprehensive performance of the electro-hydraulic position servo system under the influence of uncertain factors.

A radial basis function neural network PID controller under fuzzy rules (FUZZY-RBF-PID) was designed for the electro-hydraulic position servo system under the influence of uncertain factors such as load mutation, and load stiffness change. Firstly, the mathematical model of the system is established, and the frequency domain and time domain analysis of the system are carried out. Secondly, based on the analysis results, a radial basis function (RBF) neural network PID controller is designed, and fuzzy rules are innovatively used to adjust the learning rate of PID parameters in the RBF neural network learning algorithm in real time. Thirdly, the simulation results show that under the action of the FUZZY-RBF-PID controller, the unit step response of the system has high steady-state accuracy, fast response speed, and under the condition of large load stiffness, the system can recover to the steady-state value faster after being disturbed. At the same time, when the input signal is the sinusoidal signal of 10 HZ, the system under the action of the FUZZY-RBF-PID controller has no obvious phase lag phenomenon, and the tracking error is minimal. The proposed method can effectively improve the comprehensive performance of the electro-hydraulic position servo system under the influence of uncertain factors. [ABSTRACT FROM AUTHOR]

The stability of earth dams is assessed through safety factors, indicating stability if they exceed one. Due to soil property uncertainties, fuzzy logic tools seem suitable for slope stability analysis. Uncertainties in parameters like unit weight, cohesion, and internal friction angle can be encompassed by fuzzy set theory. This study employs fuzzy set theory to analyze slope stability factor of safety, considering the varied materials and soils in earth dams. Information and parameters were gathered, and slopes were modeled using Slide (v. 6) software. Shear strength parameters and safety factors were categorized based on results and expert opinions, defining ranges for each. MATLAB software applied fuzzy logic rules to relate inputs (unit weight, cohesion, friction angle) to the output, factor of safety. Comparing results from probabilistic and fuzzy methods revealed close numerical alignment. The fuzzy method, with adaptable rules accommodating different conditions, yielded quicker and more accurate safety assessments, assuming specific data inputs. Overall, the fuzzy approach offers flexibility, facilitating quicker and more accurate determinations of safety factors, albeit requiring specific data assumptions. [ABSTRACT FROM AUTHOR]

To improve the accuracy and robustness of autonomous vehicle localization in a complex environment, this paper proposes a multi-source fusion localization method that integrates GPS, laser SLAM, and an odometer model. Firstly, fuzzy rules are constructed to accurately analyze the in-vehicle localization deviation and confidence factor to improve the initial fusion localization accuracy. Then, an odometer model for obtaining the projected localization trajectory is constructed. Considering the high accuracy of the odometer's projected trajectory within a short distance, we used the shape of the projected localization trajectory to inhibit the initial fusion localization noise and used trajectory matching to obtain an accurate localization. Finally, the Dual-LSTM network is constructed to predict the localization and build an electronic fence to guarantee the safety of the vehicle while also guaranteeing the updating of short-distance localization information of the vehicle when the above-mentioned fusion localization is unreliable. Under the limited arithmetic condition of the vehicle platform, accurate and reliable localization is realized in a complex environment. The proposed method was verified by long-time operation on the real vehicle platform, and compared with the EKF fusion localization method, the average root mean square error of localization was reduced by 66%, reaching centimeter-level localization accuracy. [ABSTRACT FROM AUTHOR]

In recent years, toughened glass has become widely used in architectural applications due to its high structural strength, shatter resistance, soundproofing, heat resistance, and durability. However, despite its strong capabilities, toughened glass can explode completely when subjected to heavy impact, causing security-related constraints in areas prone to smash-and-grab attempts. This issue can only be prevented by enhancing the quality of toughened glass, particularly its tensile strength, compressive strength, and durability. To achieve this goal, a novel technique called the "fuzzy hybrid arithmetic war strategy" (FHAWS) approach is proposed in this paper. The approach utilizes the "hybrid arithmetic war strategy" (HAWS) algorithm to determine the optimal solution with a faster convergence rate. The HAWS algorithm integrates the standard Arithmetic Optimization (AO) algorithm and War Strategy Optimization (WSO) algorithm. The fuzzy rule concept is introduced for regulating the improper searching algorithm. The proposed FHAWS approach optimizes toughened glass characteristics such as splitting strength, flexural strength, tensile strength, compressive strength, stress, temperature, fragmentation, heating time, cooling time, visual light, durability, and production cost. The experimental analysis illustrates that the proposed FHAWS approach is more efficient and robust in enhancing the quality of toughened glass than other compared state-of-the-art approaches. Overall, the paper proposes a novel approach to optimize the characteristics of toughened glass, addressing a critical issue in the architectural industry. However, the specific details of the approach and experimental results should be examined in the paper to assess the method's effectiveness comprehensively. [ABSTRACT FROM AUTHOR]

Imbalanced datasets are common in practical applications, and oversampling methods using fuzzy rules have been shown to enhance the classification performance of imbalanced data by taking into account the relationship between data attributes. However, the creation of fuzzy rules typically depends on expert knowledge, which may not fully leverage the label information in training data and may be subjective. To address this issue, a novel fuzzy rule oversampling approach is developed based on the learning vector quantization (LVQ) algorithm. In this method, the label information of the training data is utilized to determine the antecedent part of If-Then fuzzy rules by dynamically dividing attribute intervals using LVQ. Subsequently, fuzzy rules are generated and adjusted to calculate rule weights. The number of new samples to be synthesized for each rule is then computed, and samples from the minority class are synthesized based on the newly generated fuzzy rules. This results in the establishment of a fuzzy rule oversampling method based on LVQ. To evaluate the effectiveness of this method, comparative experiments are conducted on 12 publicly available imbalance datasets with five other sampling techniques in combination with the support function machine. The experimental results demonstrate that the proposed method can significantly enhance the classification algorithm across seven performance indicators, including a boost of 2.15% to 12.34% in Accuracy, 6.11% to 27.06% in G-mean, and 4.69% to 18.78% in AUC. These show that the proposed method is capable of more efficiently improving the classification performance of imbalanced data. [ABSTRACT FROM AUTHOR]

The main weakness of complex and sizeable fuzzy rule systems is the complexity of data interpretation in terms of classification. Classification interpretation can be affected by reducing rules and removing important rules for several reasons. Based on the results of experiments using the fuzzy grid partition (FGP) approach for high-dimensional data, the difficulty in generating many fuzzy rules still increases exponentially as the number of characteristics increases. The solution to this problem is a hybrid method that combines the advantages of the rough set method and the FGP method, which is called the fuzzy grid partition rough set (FGPRS) method. In the Irish data, the rough set approach reduces the number of characteristics and objects so that data with excessive values can be minimized, and the fuzzy rules produced using the FGP method are more concise. The number of fuzzy rules produced using the FGPRS method at K=2 is 50%; at K=K+1, it is reduced by 66.7% and at K=2 K, it is reduced by 75%. Based on the findings of the data collection classification test, the FGPRS method has a classification accuracy rate of 83.33%, and all data can be classified. [ABSTRACT FROM AUTHOR]

Cruise ships are densely populated ecosystems where infectious diseases can spread rapidly. Hence, early detection of infected individuals and risk assessment (RA) of the disease transmissibility are critical. Recent studies have investigated the long-term assessment of transmission risk on cruise ships; however, short-term approaches are limited by data unavailability. To this end, this work proposes a novel short-term knowledge-based method for RA of disease transmission based on fuzzy rules. These rules are constructed using knowledge elicited from domain experts. In contrast to previous approaches, the proposed method considers data captured by several sensors and the ship information system, according to a recently proposed smart ship design. Evaluation with agent-based simulations confirms the effectiveness of the proposed method across various cases. [ABSTRACT FROM AUTHOR]

The stability of earth dams is assessed through safety factors, indicating stability if they exceed one. Due to soil property uncertainties, fuzzy logic tools seem suitable for slope stability analysis. Uncertainties in parameters like unit weight, cohesion, and internal friction angle can be encompassed by fuzzy set theory. This study employs fuzzy set theory to analyze slope stability factor of safety, considering the varied materials and soils in earth dams. Information and parameters were gathered, and slopes were modeled using Slide (v. 6) software. Shear strength parameters and safety factors were categorized based on results and expert opinions, defining ranges for each. MATLAB software applied fuzzy logic rules to relate inputs (unit weight, cohesion, friction angle) to the output, factor of safety. Comparing results from probabilistic and fuzzy methods revealed close numerical alignment. The fuzzy method, with adaptable rules accommodating different conditions, yielded quicker and more accurate safety assessments, assuming specific data inputs. Overall, the fuzzy approach offers flexibility, facilitating quicker and more accurate determinations of safety factors, albeit requiring specific data assumptions.

Digitalization occupies entire world irrespective of the fields including the construction field study and it plays a major role to identify the better structure using the various design software that are available in the market. The identification of suitable connection is very important and tedious task for the structural engineers. For fulfilling the structural engineers' requirements, this study proposes a new Intelligent Fuzzy Rule-based Connection Prediction Method (IFR-CPM) to predict the types of joints for the extended endplate long bolted joint by applying the newly generated fuzzy rules. Here, the fuzzy rules have been generated by considering the different values of moments and rotations to find the type of joints. The proposed prediction system is evaluated by conducting different experiments and proved as better than the existing system with respect to the prediction accuracy. [ABSTRACT FROM AUTHOR]

In the current scenario, renewable energy sources (RES) are used in transport applications to minimise the dependency on fossil fuels. The electrical vehicle (EV) has contributed a vital role in smart grids with the penetration of RES. The battery connected EVs have increased in the past few years owing to numerous benefits. Thus, it is necessary to develop an optimum charging controller for the charging station to meet the energy demand. In order to accomplish this goal, hybrid fuzzy fractional order proportional integral derivative (HF2OPID) is proposed. Meanwhile, the controlling parameters of the HF2OPID are tuned by all member based optimisation algorithms (AMBO). Alongside, a hybrid honey badger recurrent neural network (H2B-RNN) provides peak power from the solar photovoltaic (SPV) module. The honey badger optimisation algorithm examines the optimum weights of the layers. The proposed method is implemented on the MATLAB/Simulink platform, and the results are compared with the existing methods. The obtained results demonstrate the proposed method's compatibility with the available techniques in the literature. [ABSTRACT FROM AUTHOR]

This study presents a machine vision-based variable weeding system for plant- protection unmanned ground vehicles (UGVs) to address the issues of pesticide waste and environmental pollution that are readily caused by traditional spraying agricultural machinery. The system utilizes fuzzy rules to achieve adaptive modification of the Kp, Ki, and Kd adjustment parameters of the PID control algorithm and combines them with an interleaved period PWM controller to reduce the impact of nonlinear variations in water pressure on the performance of the system, and to improve the stability and control accuracy of the system. After testing various image threshold segmentation and image graying algorithms, the normalized super green algorithm (2G-R-B) and the fast iterative threshold segmentation method were adopted as the best combination. This combination effectively distinguished between the vegetation and the background, and thus improved the accuracy of the pixel extraction algorithm for vegetation distribution. The results of orthogonal testing by selected four representative spraying duty cycles—25%, 50%, 75%, and 100%—showed that the pressure variation was less than 0.05 MPa, the average spraying error was less than 2%, and the highest error was less than 5% throughout the test. Finally, the performance of the system was comprehensively evaluated through field trials. The evaluation showed that the system was able to adjust the corresponding spraying volume in real time according to the vegetation distribution under the decision-making based on machine vision algorithms, which proved the low cost and effectiveness of the designed variable weed control system. [ABSTRACT FROM AUTHOR]

Making panoramic images has gradually become an essential function inside personal intelligent devices because panoramic images can provide broader and richer content than typical images. However, the techniques to classify the types of panoramic images are still deficient. This paper presents novel approaches for classifying the photographic composition of panoramic images into five types using fuzzy rules. A test database with 168 panoramic images was collected from the Internet. After analyzing the panoramic image database, the proposed feature model defined a set of photographic compositions. Then, the panoramic image was identified by using the proposed feature vector. An algorithm based on fuzzy rules is also proposed to match the identification results with that of human experts. The experimental results show that the proposed methods have demonstrated performance with high accuracy and this can be used for related applications in the future. [ABSTRACT FROM AUTHOR]

Decision and planning are key technologies for achieving autonomous driving. In response to the issue of considering the safety of the target lane and planning the optimal obstacle avoidance trajectory for autonomous trucks during emergency turning and obstacle avoidance, a fuzzy relationship was established based on the difference between the relative distance between vehicles on the left and right lanes during lane changing and the minimum safe distance between the vehicles during lane changing. The safety values designed by fuzzy rule reasoning were compared, a safer lane for turning was selected to avoid obstacles. In order to quickly plan the optimal obstacle avoidance trajectory, a third-order Bézier curve was used to form an obstacle avoidance curve by designing the coordinates of four control points. In order to prevent the truck from rollover and collision with the vehicle in front due to excessive lateral acceleration during turning, the stability boundary and collision boundary of the vehicle were designed to constrain the control points, the functions in MATLAB was used to solve the optimal lane changing trajectory at different speeds, and finally simulation software was used for simulation verification. The results show that the designed obstacle avoidance decision and trajectory planning can safely and effectively avoid obstacles. [ABSTRACT FROM AUTHOR]

Actuator attacks on the steering control actions of Autonomous Vehicles (AVs) will disrupt the control actions of path following, even lead to a serious traffic accident. In order to ensure the secure path following of AVs, a fuzzy-rule-based global fast terminal sliding mode control (FR-GFTSMC) approach is designed for mitigating the malicious actuator attacks imposed on the steering gear. Firstly, the global fast terminal sliding mode control scheme is utilized to counteract the effects caused by actuator attacks while guaranteeing to reach a steady state in a finite time of the regulated steering actions. Then, the attack-aware-based fuzzy rules are well exploited to improve the control performance by mitigating the chattering caused by switching of sliding controller. At last, Simulink-CarSim co-simulations are used to verify the effectiveness of the proposed secure steering control strategy.

In this study, we introduce an innovative network architecture that synergizes fuzzy neural networks with positional self-attention mechanisms to enhance fake news detection. This approach effectively addresses emerging challenges posed by new fake news technologies, aiming to bolster detection accuracy, protect public interests, and support credible media development. By integrating diverse information sources, including textual content and semantic nuances, our model excels in processing ambiguous data and discerning subtle variances in news authenticity. The utilization of fuzzy neural networks allows for adept handling of uncertain information, while positional self-attention coding proficiently identifies the significance of different textual elements, offering a nuanced analysis of news veracity. Our extensive experiments on two datasets reveal a substantial improvement in detection accuracy, with the model achieving an accuracy increase of over 15% compared to traditional methods. This work not only demonstrates a methodological advancement in tackling fake news but also contributes significantly to upholding social integrity and public trust.

ObjectiveIn order to solve the problem of an underactuated ship responding slowly to heading changes during rudder roll stabilization (RRS) caused by fixed weight values in model predictive control (MPC), a RRS control method based on the finite time extended states observer (FTESO), fuzzy rules and robust predictive control is proposed. MethodsA fixed speed linear underactuated ship model is established for controller design. The FTESO is used to estimate the ship's motion states and external disturbances. By analyzing the conditions of the ship's course-keeping and heading change, the objective function weights under the two conditions and the fuzzy rules between the states and weights are designed respectively. Robust predictive control is used to solve the multi-objective cooperative control problem with constraints. The closed-loop stability of the proposed control method is then proven theoretically. ResultsAccording to a numerical simulation of a multi-purpose naval vessel, the proposed control method is compared with a disturbance compensation MPC and disturbance observer enhanced MPC, and is shown to have a higher roll stabilization rate by 5.74% and 0.898 3%, respectively. The response time of the proposed method for a 30° heading change is also reduced by 1.8 s and 7.3 s respectively. ConclusionThe effectiveness of the proposed method in underactuated ship rolling reduction is proven.

Today, PID controllers are widely employed in a wide variety of manufacturing processes to regulate critical inputs. In this study, an ANFIS-PID controller with adaptive tuning parameters is introduced. This design works well for a nonlinear, closed-loop system with two inputs and two outputs (known as a TITO system). The key benefit here is incorporating the Sugeno function, which is based on the equation of conventional PID control and decoupling coefficients, into the fuzzy rules. Because of this, the decoupling ANFIS- PID controller that was developed may be seen as a natural analogue to the traditional one with decoupling components. The paper considers the implementation of a TITO nonlinear system to demonstrate the merits of the design paradigm. The proposed method is been validated using simulation results.

In this paper, a method to analyse load features utilising sample shifting technique (SST) for non-intrusive load identification (NILI) is presented and discussed. Fuzzy rules are used as the foundation for the identification logic. Voltage and current signals for electrical home appliances are acquired in order to develop their respective features. Two features like reactive power and total harmonic distortion for current (THD I), are created with the necessary computations of the samples using SST. A method based on fuzzy rules is created in order to identify different electrical equipment both for their individual as well as simultaneous running. Again, the performance of the proposed system is tested under the noisy environment while the accuracy of the system is found satisfactory. By utilising SST, the burden of computation is reduced in comparison to the other methods which are justified with the experimental results. • Only voltage and current information are used for load identification. • Sample Shifting Technique is used to extract load features. • Reactive power and THD for current are used as load features. • Fuzzy logic-based rules are applied for non-intrusive load identification. • The use of of SST assists in the extraction of load features and reduces the complexity of the hardware by eliminating the need for filters or ZCDs. • Individual load can be identified during practical multiple loads are working parallelly. • System shows robustness under noisy environment. [ABSTRACT FROM AUTHOR]

The real world is nonlinear and in the control application field, this aspect needs to be resolved to build models so we need to refer to nonlinear system modeling techniques. Neuro-fuzzy systems and modular neural networks (NNs) are among the best modeling approaches for nonlinear systems. The combined features of both approaches provide better models. Thus, we propose in this paper a neuro-fuzzy modular architecture for modeling nonlinear systems. The modular architecture consists of dividing a nonlinear problem into several simpler subproblems. We assigned to each subproblem an NN. Each NN provides individual solutions that will be combined to provide a general solution to the original problem. In this respect, the decomposition of the original problem is based on a fuzzy decision mechanism. This mechanism consists of a set of fuzzy rules for processing nonlinear problems using two different strategies. The first involves training only the network weights, and the second adds the fuzzy set parameters to the training step. A comparative study of both strategies reveals the competence of the second strategy in providing better accuracy and simplicity. Using the neuro-fuzzy combination among the modular NNs reduces the complexity of the original problem and achieves much better performance. The proposed architecture is evaluated by two second-order nonlinear systems, a numerical system and a real system called "the chemical reactor," which is used to carry out a chemical reaction not only in chemical and biochemical engineering, but also in the petrochemical industry. For both systems, the proposed approach provides better performance in terms of the learning time, learning error, and number of neurons. [ABSTRACT FROM AUTHOR]

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After establishing a fuzzy rule base using the interpolation result mechanism, arranging a fuzzy inference system is performed. The established system will infer about the level of risk using a fuzzy rule base and input statistics. In this work, the construction of the inference system has been determined using fuzzy rules interpolation. The construction determines the distances between input signals and relation functions based on if-then rules. The cross section a used here reduces the distance between input variables. In this work, interpolation is used to measure the distance between a-section fuzzy sets. Such economics issues as forecasting, planning, calculation of expected income are considered in uncertainty conditions, incomplete information conditions using interpolation. [ABSTRACT FROM AUTHOR]

In recent years, agriculture has incorporated various sensors to obtain crop data. Nutrients, conductivity, and pH in soil are the key factors affecting the growth of crops. Before using fertilizers, farmers need to master the soil information of the orchard to decide the ratio of nitrogen, phosphorus, and potassium to be fertilized and the amount of water to be irrigated. Although existing outdoor technology sensing solutions can detect soil nutrient information, there are high maintenance costs and systems that do not provide analysis recommendations, which are very unfriendly to farmers. Aiming at the above problems, we propose the fuzzy neural network of fertilization rate and fuzzy neural network of fertilization rate to provide farmers with the most direct fertilization and irrigation suggestions. This paper uses an automated self-propelled soil identification system that enables path planning in an orchard. After arriving at the measurement point designated by the farmer, we use the soil probe measurement system to measure soil nutrients within a certain range. The system provides farmers with suggestions on fertilization and medication and grasps the growth status of the orchard area. The advantage of this is that it can reduce the maintenance cost caused by the long-term installation of the system. The cost of our vehicle-mounted system is 150,000 NT dollars, which saves 350,000 NT dollars a year and 50,000 NT dollars in maintenance costs compared to installing ten outdoor weather station sensors. Compared with fixed weather station solutions, mobile unmanned vehicles can analyze more land areas and bring more precise benefits to farmers. [ABSTRACT FROM AUTHOR]

The traditional PID algorithm used in the currently existing automatic train operation system is limited due to fixed parameters, making it difficult to achieve excellent control effects in actual operation scenes featuring strong coupling and high nonlinearity, which is mainly attributed to difficulties in overcoming nonlinear disturbances.In light of this, this paper proposes an automatic train operation approach that relies on a fuzzy adaptive PID algorithm, which can adjust the PID parameters in real time according to the preset fuzzy rules, thus improving the PID controller's performance in speed tracking and leading to an improved train control effect. Based on the data from Changsha Rail Transit Line 4, the semi-physical simulation results show that the proposed algorithm resulted in the average root-mean-square error (RMSE) of 18.876 cm/s between the actual train speed and the recommended speed, which is less than the 35.200 cm/s of the traditional PID controller and the value of fitting degree is 1.69 m/s between travel speed and the recommended speed, which is less than the 2.25 m/s of the traditional PID controller, suggesting that the proposed algorithm offers a more effective solution for tracking the recommended speed curves and improving system operation efficiency.

To improve the accuracy and robustness of autonomous vehicle localization in a complex environment, this paper proposes a multi-source fusion localization method that integrates GPS, laser SLAM, and an odometer model. Firstly, fuzzy rules are constructed to accurately analyze the in-vehicle localization deviation and confidence factor to improve the initial fusion localization accuracy. Then, an odometer model for obtaining the projected localization trajectory is constructed. Considering the high accuracy of the odometer’s projected trajectory within a short distance, we used the shape of the projected localization trajectory to inhibit the initial fusion localization noise and used trajectory matching to obtain an accurate localization. Finally, the Dual-LSTM network is constructed to predict the localization and build an electronic fence to guarantee the safety of the vehicle while also guaranteeing the updating of short-distance localization information of the vehicle when the above-mentioned fusion localization is unreliable. Under the limited arithmetic condition of the vehicle platform, accurate and reliable localization is realized in a complex environment. The proposed method was verified by long-time operation on the real vehicle platform, and compared with the EKF fusion localization method, the average root mean square error of localization was reduced by 66%, reaching centimeter-level localization accuracy.

Twin‐roll strip casting is a near‐net‐shape casting technology that can produce thin steel strips directly from molten steel. Stably controlling the molten steel level is regarded as an important issue to ensure strip quality and casting process stability. As the control of the molten steel level is a time‐varying, nonlinear, and multidisturbance complex system, it is difficult to establish an accurate process model for designing a model‐based controller. Top side‐pouring twin‐roll casting is a new kind of twin‐roll strip casting technology. This study introduces the control system of the top side‐pouring twin‐roll casting process. A fuzzy logic controller (FLC) with its fuzzy rules optimized by particle swarm optimization (PSO) is developed to regulate the molten steel level. Simulation results show that the performance of the FLC can be improved while its fuzzy rules are optimized by PSO. The objective function of PSO has a great influence on the optimization of the fuzzy rules. The top side‐pouring twin‐roll casting experiments are carried out using the FLC with its fuzzy rules optimized by PSO; the results show that strip quality and casting process stability are guaranteed. [ABSTRACT FROM AUTHOR]

Heart disease is known as the deadliest disease in the world which mostly focus on coronary diseases, cerebrovascular diseases, and ischemic heart disease. The treatment for the diseases is highly costly, and not only that, the monitoring system or devices that are in the market are low in accuracy and not satisfying. This work proposed to develop a prediction system for heart conditions using fuzzy system that is based on essential risk factors: age, gender, body mass index (BMI), blood pressure level (systolic), cholesterol level, heart rate, smoking habit, alcohol intake, eating habit and exercise. The specific fuzzy rules are created and produced in the output category of low, medium, and high risks. The proposed system was later evaluated by comparing the machine learning performance metrics such as accuracy, specificity, sensitivity and F1 score. It is found that the accuracy, sensitivity, specificity and F1 score are calculated as 88.2%, 78.8%, 21.2%, and 80.9%, respectively, which demonstrates a reliable percentage score. It is believed that this work has the potential to be an alternative method in providing as a dependable and cheap means of predicting heart disease. [ABSTRACT FROM AUTHOR]

The washing machine is the most commonly utilized household appliance. The automatic washing machines are developed using Fuzzy Logic Controller by several authors. The amount of water, electricity and detergent are more effective parameters for developing economical washing machines. In this work, we design an economical fuzzy logic controller for washing machine which optimize consumption of water, electricity and amount of detergent. We employ Mamdani approach to develop the algorithm as this approach gives higher accuracy. In the fuzzy-rule basis system, 36 rules are created based on expert user knowledge. The algorithm has been implemented and simulated using python programming language. The simulation results show that the proposed algorithm for washing machine provides better performance at a lower computational cost. The comparative analysis of proposed work with the previous work shows that our input-output control system optimises performance of the washing machine. [ABSTRACT FROM AUTHOR]

This paper proposes a novel positioning and anti-swing controller based on fuzzy sliding mode for an overhead crane. For the underactuated characteristics of the overhead crane, the trolley displacement and load swing angle are integrated into the same sliding mode surface. The boundness and convergence of each state on the sliding mode surface are analyzed. Appropriate fuzzy rules are introduced to adjust the control quantity to ensure that the system state is always on this surface. The sliding mode function is used to replace the tracking error signal to reduce the dependence of the control method on the system model, so that the control system has better robustness to parameter changes and external disturbances. The experiment and results analysis show that this proposed method can effectively deal with the dynamic deviation of the system model and external random disturbances. The load swing has a good suppression effect and the positioning of the trolley can be accomplished quickly and accurately. The proposed positioning and anti-swing controller based on fuzzy sliding mode is able to provide the high-quality and fast transportation of overhead cranes. [ABSTRACT FROM AUTHOR]

The aim of this work is to study the influence of the number of membership functions (MF) of fuzzy logic controller (FLC) on the temporal performances of dynamical systems. A second contribution to this idea is the introduction of the disturbance signal as an additional input to the FLC and this makes it possible to deliver a command taking into account the values of this undesirable signal. In order to illustrate the influence of the number of membership functions of an FLC, the angular position in a linear model of an Autonomous Underwater Vehicle (AUV) will be controlled around a given reference. The simulation results show that it is sufficient to limit the number of membership functions to a maximum of 5 MF and this is interpreted by the boundary fuzzy decision surface of the fuzzy control because there is no influence, in the case of adding other supplementary MFs, on the performances of the system.