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Many advanced industrial processes are a class of time-varying distributed parameter systems (DPSs). It is not an easy task for traditional spatiotemporal modeling methods to approximate these systems because of the inherent time-varying and strong nonlinear characteristics. To address this problem, a novel online spatiotemporal modeling method using Kernel-based Multilayer Extreme Learning Machine is proposed to model the time-varying DPSs. First, the Kernel-based Multilayer Extreme Learning Machine is designed to create a deep network through stacking multiple Kernel-based Extreme Learning Machine Autoencoders and one original Extreme Learning Machine Autoencoder. In this step, the spatiotemporal output of time-varying DPSs is transformed into low-dimensional time coefficients directly. Then Online Sequential Regularized Extreme Learning Machine is developed to predict temporal dynamics of time-varying DPSs. Finally, based on the temporal dynamics model, Kernel-based Extreme Learning Machine is applied to reconstruct the spatiotemporal dynamics. Simulations on the thermal processes of a lithium-ion battery and a snap curing oven are presented to validate the performance and effectiveness of the proposed modeling method.

The implementation of novel machine learning models can contribute remarkably to simulating the degradation of concrete due to environmental factors. This study considers the sulfuric acid corrosive factor in wastewater systems to simulate concrete mass loss using five machine learning models. The models include three different types of extreme learning machines, including the standard, online sequential, and kernel extreme learning machines, in addition to the artificial neural network, classification and regression tree model, and statistical multiple linear regression model. The reported values of concrete mass loss for six different types of concrete are the target values of the machine learning models. The input variability was assessed based on two scenarios prior to the application of the predictive models. For the first assessment, the machine learning models were developed using all the available cement and concrete mixture input variables; the second assessment was conducted based on the gamma test approach, which is a sensitivity analysis technique. Subsequently, the sensitivity analysis of the most effective parameters for concrete corrosion was tested using three different approaches. The adopted methodology attained optimistic and reliable modeling results. The online sequential extreme learning machine model demonstrated superior performance over the other investigated models in predicting the concrete mass loss of different types of concrete.

With the development of “Internet+”, online auction platforms of used cars have emerged a lot. As a typical representative of the continuous purchase environment, online sequential auction of used cars faces many uncertainties, including uncertain revenue and risk. To describe them, adopting fuzzy theory to create mean-variance model to estimate the revenue and risk is showed in this paper. Moreover, three types of sellers, aggressive, conservative and rational sellers are analyzed respectively, and strategy models are built, where the multi-criteria optimal function for the latter one is adapted Cobb-Douglas production function. Then, a genetic algorithm based on fuzzy simulation is proposed through integrating the fuzzy simulation and 0-1 genetic algorithm, which can solve the models validly. Lastly, the practical example from Guazi website shows the optimal strategies derived by models can meet sellers’ demands, especially goals of both higher revenue and lower risk for rational sellers, which proves practicability of the model and validity of algorithm.

As the beginning of rare earth industry, several days are normally required for characterization of rare earth elements (REEs) fractionation in ore samples. Herein, rapid fractionation analysis of 15 REEs and accompanied metal (Fe, Mn, etc.) in ore samples has been achieved within 1 h using ICP-MS with a homemade device for online sequential solvent extraction. As a result, five fractionations for REEs occurrences, i.e., water soluble, exchangeable, reducible, oxidizable and crystalline, have been identified, offering chemical insights which not only reveal the formation mechanism of REEs ores but also show great implications for guiding the exploitation and separation of REEs. In comparison with conventional methods, the present approach significantly shortened the analysis time (1 h vs. ∼80 h) and reduced the sample consumption (1.0 mg vs. 5.0 g) with high recovery (>95%), providing a useful platform for the rapid quantitative fractionation analysis of REEs in complexed samples such as ore and fossils.

We investigate monopoly profit under a new online tying strategy, namely sequential bundling. This method allows customers to buy additional products at discounted prices immediately after purchasing one or some of the available products. This strategy has been practiced by Walmart and others but has not been modelled to date. We use microeconomics analysis to conduct a comparison of the gained profit with the three commonly used bundling strategies: no bundling, pure bundling and mixed bundling. The main result shows that the sequential bundling strategy yields higher profits in comparison to all three classic strategies. In particular, for the symmetric cost case, our model provides a useful tool for practitioners such as common online shops.

One of the main issues of wireless sensor networks is localization. Besides, it is important to track and analyze the sensed information. The technique of localization can calculate node position with the help of a set of designed nodes, denoted as anchors. The set density of these anchors may be incremented or decremented because of many reasons such as maintenance, lifetime, and breakdown. The well-known Distance Vector Hop (DV-Hop) algorithm is a suitable solution for localizing nodes having few neighbor anchors. However, existing DV-Hop-based localization methods have not considered the problem of anchor breakdown which may happen during the localization process. In order to avoid this issue, an Online Sequential DV-Hop algorithm is proposed in this paper to sequentially calculate positions of nodes and improve accuracy of node localization for multihop wireless sensor networks. The algorithm deals with the variation of the number of available anchors in the network. We note that DV-Hop algorithm is used in this article to process localization of nodes by a new optimized method for the estimation of the average distance of hops between nodes. Our proposed localization method is based on an online sequential computation. Compared with the original DV-Hop and other localization methods from the literature, simulation results prove that the proposed algorithm greatly minimizes the average of localization error of sensor nodes.

In today’s world, data is produced at a very high speed and used in a large number of prediction problems. Therefore, the sequential nature of learning algorithms is in demand for batch learning algorithms. This paper presents a novel online sequential algorithm for extreme learning machine with l2,1-norm regularization (LR21OS-ELM) to handle the real-time sequential data. Wang et al. have given ELM with l2,1-norm based regularization namely LR21-ELM. This method is a batch processing model which takes data in a single chunk. So, whenever a new chunk of data arrives the model has to be retrained which takes a lot of time and memory. The proposed sequential algorithm does not require building a new model each time data arrives. This will update the previous model with new data that will save time and memory. The l2,1-norm regularization is a structural sparse-inducing norm which is integrated with an online sequential learning algorithm to diminish the complexity of the learning model by eliminating the redundant neurons of OS-ELM model. This paper proposes an iterative bi-objective optimization algorithm to solve l2,1 norm-based minimization problem and to handle the real time sequential data. The proposed model can learn sequentially arriving data in the form of chunks where chunk size can be fixed or varying. The experimental study has been conducted on several benchmark datasets collected from different research domains to prove the generalization ability of the proposed algorithm. The obtained results show that LR21OS-ELM combines the advantages of l2,1-norm regularization and online sequential learning of data and improves the prediction performance of the system.

The characteristics of the data stream have brought enormous challenges to classification algorithms. Concept drift is the most concerning characteristics, and developed classification algorithms must tackle the concept drift problem. Therefore, Extreme Learning Machines (ELM) based algorithms have been developed to respond to the characteristics of the data stream. However, due to randomly assigned input layer weights, ELM based algorithms have encountered problems such as producing inconsistent outputs, generating ill-conditioned matrix, and mapping the inputs to the worst representative space. To overcome these problems, this paper aims to build a stable and well-constructed classifier that responds to the requirements of the data stream by considering all characteristics. A novel data stream classification approach based online sequential ELM (OS-ELM) with unsupervised feature representation learning (UFROS-ELM) and ensemble UFROS-ELM approach based on majority learning is presented in this paper. UFROS-ELM is a modification of the OS-ELM with ELM-AE and concept drift mechanism. ELM-AE is utilized for computing the best discriminative input weights of the classifier. The classifier is then initialized by using the determined weights, first chunk, and OS-ELM algorithm. When a new data chunk arrives, the classifier firstly searches any concept drift occurrence. If it is detected, ELM-AE is utilized to reconstruct the classifier to adapt to the changes. Otherwise, the classifier is sequentially updated updates by processing the current chunk. The results are achieved on the well-known real and artificial data sets and compared with state-of-the-art data stream classification algorithms. The experimental studies demonstrate the achievements of the proposed approaches.

Indoor localization technology has recently attracted an increasing attention in research, among which radio frequency identification (RFID) technology has become a preferred solution due to its advantages in non-line of sight, non-contact and rapid identification. However, in the practical RFID indoor localization application scenarios, the RFID readers need to keep working for an extensive time. When some readers malfunction and fail to be repaired in time, the existing algorithms usually cannot maintain the accuracy of the original localization system. In this paper, we propose an RFID reader-fault-adaptive localization algorithm based on online sequential fuzzy broad learning system, called RF-OSFBLS algorithm. The RF-OSFBLS algorithm improves the fuzzy broad learning system (FBLS) with the ability of online sequential learning (OSFBLS) by using the updating algorithm, so that it can process data streams that continue to arrive in the environment. Meanwhile, RF-OSFBLS algorithm proposes RFID reader-fault-adaptive strategy by introducing a transformation matrix, which can process subsequent data streams when reader fault occurs. We have carried out experiments to study the influence factors and validate the performance, both the simulation and realistic experiment results show that our proposed RF-OSFBLS algorithm can achieve better positioning effect and maintain a relatively high accuracy in the dynamically changing and reader-fault environment.

Because it is difficulty to classify level of fabric wrinkle, this paper proposes a fabric winkle level classification model via online sequential extreme learning machine based on improved sine cosine algorithm (SCA). The SCA has excellent global optimization ability, can explore different search spaces, and effectively avoid falling into local optimum. Because the initial population of SCA will have an impact on its optimization speed and quality, the SCA is initialized by differential evolution (DE) to avoid local optimization, and then the output weight and hidden layer bias are optimized; that is, the improved SCA is used to select the optimal parameters of the online sequential extreme learning machine (OSELM) to improve the generalization performance of the algorithm. To verify the performance of the proposed model DE-SCA-OSELM, it will be compared with other algorithms using a fabric wrinkles dataset collected under standard conditions. The experimental results indicate that the proposed model can effectively find the optimal parameter value of OSELM. The average classification accuracy increased by 6.95%, 3.62%, 6.67%, and 3.34%, respectively, compared with the partial algorithms OSELM, SCAELM, RVFL and PSOSVM, which meets expectations.

Online sequential monitoring of the incidence rates of chronic or infectious diseases is critically important for public health. Governments have invested a great amount of money in building global...

This chapter presents a methodology for the generation of paths from surveillance ground maps. The methodology detects and monitors texture patterns across consecutive images using an online sequential algorithm that creates piecewise linear approximation of the monitored trajectory. It takes under consideration the local structure of the patterns in order to extract a local path. The overall global path is sequentially formed from each segment. We present the preprocessing steps used to extract patterns of interest from an image and the results from two case study examples. The methodology has applications in the autonomous path planning of aerial vehicles following a specified structure, such as that of a river or road.

Reward can increase the speed and accuracy of movements in both simple and sequential reaching tasks. Two mechanisms are thought to be responsible for this: an increase in maximum velocity, due to increased muscle stiffness, resulting in faster, but energetically inefficient, individual movements; or coarticulation – the blending of sub-movements into single, smoother, more energetically efficient movements. Older adults have shown reduced sensitivity to reward in decision paradigms, but there is little research relating reward and motor performance in older adults. Using a novel online sequential reaching task, we compared the effects of reward on motor performance between young (18-23 years) and older (65-79 years) participants. We found that movement time decreased across training in all groups, and reward invigorated this decrease in both age groups. This suggests that reward is a viable facilitator of motor performance to compensate for age-related motor decline and has the potential for use in the design of rehabilitation programmes for age-related motor deficits or disease.

Online Sequential Extreme Learning Machine (OS-ELM) has been confirmed by numerous studies to be an effective algorithm for online learning scenarios. However, we found that some parameters of OS-ELM are randomly assigned and remain unchanged in the subsequent learning process, which leads to great instability in the model performance in practice. To alleviate this problem, we propose a novel ensemble OS-ELM algorithm (EOS-ELM-R) for solving air quality prediction problems. EOS-ELM-R uses multiple distribution functions to initialize the random parameters of the base OS-ELM models and its final output is the average of the predictions of these base models. Extensive experimental results on two real-world air quality prediction problems show that EOS-ELM-R is effective, and it can achieve better generalization capabilities than similar algorithms.

Different road conditions and dynamic environment bring significant challenges to the control system of autonomous driving vehicle (ADV). As is known, historical data collected from ADV contains valuable information about control systems, therefore, it is a promising thing to study adaptive control algorithms that have certain learning ability. In order to improve the control performance of ADV and the efficiency in data usage, in this paper, a model free adaptive control algorithm based on regularized online sequential extreme learning machine (ReOSELM) is introduced, it is difficult to analyze the algorithm based on neural network, and the system stability by improved update algorithm of ReOSELM is proved. Simulation results indicate that the proposed algorithm is effective in improving control precision when ADV is turning, and experimental results on an autonomous driving vehicle show that this algorithm is effective in real environment.

Background: Transformer is one of the most important pivot equipment in an electric system which undertakes major responsibility. Therefore, it is very important to identify the fault of the transformer accurately and transformer fault diagnosis technology becomes one topic with great research value. Methods: In this paper, after analyzing the shortcomings of traditional methods, we have proposed a transformer fault diagnosis method based on Online Sequential Extreme Learning Machine (OS-ELM) and dissolved gas-in-oil analysis. This method has better precision than some commonly used methods at present. Furthermore, OS-ELM is more efficient than ELM. In addition, we analyze the effect of different parameter selection on the performance of the model by contrast experiments. Results: The experimental result shows that OS-ELM has certain promotion in precision than some traditional methods and can obviously improve the speed of training than ELM. Besides, it is known that the number of neurons in the hidden layer and the size of dataset have a great effect on the model. Conclusion: The transformer fault diagnosis method based on OS-ELM can effectively identify the faults and more efficient than ELM.

Recently, the echo state networks (ESNs) have been widely used for time series prediction. To meet the demand of actual applications and avoid the overfitting issue, the online sequential ESN with sparse recursive least squares (OSESN-SRLS) algorithm is proposed. Firstly, the l 0 and l 1 norm sparsity penalty constraints of output weights are separately employed to control the network size. Secondly, the sparse recursive least squares (SRLS) algorithm and the subgradients technique are combined to estimate the output weight matrix. Thirdly, an adaptive selection mechanism for the l 0 or l 1 norm regularization parameter is designed. With the selected regularization parameter, it is proved that the developed SRLS shows comparable or better performance than the regular RLS. Furthermore, the convergence of OSESN-SRLS is theoretically analyzed to guarantee its effectiveness. Simulation results illustrate that the proposed OSESN-SRLS always outperforms other existing ESNs in terms of estimation accuracy and network compactness.

This paper proposes a density-based semi-supervised online sequential extreme learning machine (D-SOS-ELM). The proposed method can realize online learning of unlabeled samples chunk by chunk. Local density and distance are used to measure the similarity of patterns, and the patterns with high confidence are selected by the ‘follow’ strategy for online learning, which can improve the accuracy of learning. Through continuous patterns selection, the proposed method ultimately achieves effective learning of unlabeled patterns. Furthermore, using local density and relative distance can effectively respond to the relationship between patterns. Compared with the traditional distance-based similarity measure, the ability to deal with complex data is improved. Empirical study on several standard benchmark data sets demonstrates that the proposed D-SOS-ELM model outperforms state-of-art methods in terms of accuracy.

In the present study, we propose the application of two artificial intelligence models, namely: (i) the optimally pruned extreme learning machine (OPELM); and (ii) the online sequential extreme learning machine (OSELM) models, for estimating daily pan evaporation (Epan). The two models were developed and compared using four climatic data collected at two stations: Ain Dalia and Zit Emba. The maximum and minimum temperatures (Tmax, Tmin), wind speed (U2), relative humidity (RH %) and Epan data were used as inputs to the models. Pan evaporation Epan was directly measured using Class A evaporation pan. The results show that the two models provided different results at the two stations: the OPELM worked well at Ain Dalia while OSELM was more accurate at Zit Emba. More importantly, the inclusion of the periodicity did not lead to a significant improvement in the accuracy of the models. OSELM validation results, with a coefficient of correlation R = 0.872, a root mean square error RMSE =1.698 mm, and a mean absolute error MAE = 1.311 mm outperformed OPELM (R = 0.853, RMSE = 1.813 mm and MAE = 1.403 mm) at Zit Emba. In addition, at Ain Dalia, the results indicate that OPELM model provided slightly higher prediction accuracy compared to the OSELM model (R = 0.808 against 0.800; RMSE = 1.447 mm against 1.471 mm; MAE = 1.091 mm against 1.084 mm). This work demonstrates the ability of the OPELM and OSELM approaches for estimating daily Epan using easily measured climatic variables.

Online sequential extreme learning machine (OS-ELM) and its variants provide a promising way to solve data stream problems, but most of them do not take the timeliness of the problems into account, which may degrade the performance of the model. The main reason is that these algorithms are unable to adapt to the latest data accordingly when the distribution of the data stream changes. To mitigate this limitation, the forgetting factor is introduced into the relevant models, which is used to balance the relative importance of past data and new data when necessary. However, there is no efficient way to set the forgetting factor properly so far. In this paper, we have developed a novel updating strategy for setting the forgetting factor and proposed a dynamic forgetting factor based OS-ELM algorithm (DOS-ELM). In the sequential learning phase of DOS-ELM, the forgetting factor can be adjusted dynamically according to the change degree of the model accuracy in each learning epoch. This updating process does not require setting any parameters artificially and thus greatly improves the flexibility of the model. The experimental results on ten classification problems, five regression problems, one time-series problem show that DOS-ELM can deal well with both stationary and non-stationary data stream problems. In addition, we have extended DOS-ELM to an online deep model named ML-DOS-ELM, which can handle more complex tasks such as the face recognition problem and the handwritten digit recognition problem. Our experimental evaluations show that both DOS-ELM and ML-DOS-ELM can achieve higher prediction accuracy compared to the other similar algorithms.

Researches on computer games for Go, Chess, and Japanese Chess stand out as one of the notable landmarks in the progress of artificial intelligence. AlphaGo, AlphaGo Zero, and AlphaZero algorithms, which are called AlphaZero style (AZ-style) algorithms in some literature [1] , have achieved superhuman performance by using deep reinforcement learning (DRL). However, the unavailability of training details, expensive equipment used for model training, and the low evaluation accuracy resulted by slow self-play training without expensive computing equipment in practical applications have been the defects of AZ-style algorithms. To solve the problems to a certain extent, the paper proposes an improved online sequential extreme learning machine (IOS-ELM), a new evaluation method, to evaluate chess board positions for AZ-style algortihm. Firstly, the theoretical principles of IOS-ELM is given. Secondly, the study considers Gomoku as the application object and uses IOS-ELM as the evaluation method for AZ-style’s board positions to discuss the loss in the training process and hyperparameters affecting performance in detail. Under the same experimental conditions, the proposed method reduces the training parameters by 14 times, training time to 15%, and error of evaluation by 13% compared with the board evaluation network used in original AZ-style algorithms.

Integrated modular avionics is one of the most advanced systems which has been widely applied in modern aircraft. The performance of integrated modular avionics deeply impacts flight mission. Remaining useful life prediction is the critical manner which can efficiently improve the safety and reliability of aircraft. Since integrated modular avionics is a real-time system, the prediction algorithm should have fast learning speed to satisfy the real-time requirement. In this paper, an enhanced online sequential method is proposed to predict the remaining useful life of integrated modular avionics. Firstly, this paper proposes an online sequential network which is based on the architecture of parallel layer network. Secondly, to enhance the learning capability, this paper adopts the extreme learning machine denosing autoencoder to determine the input weights of the network. Thirdly, an adaptive forgetting factor is added to further improve the performance of the proposed method. The effectiveness and the superiority of the proposed method are verified in comparison with three online sequential algorithms on the standard datasets. Finally, a degradation model is built to depict the deteriorated process of integrated modular avionics. The prediction results confirms that the proposed method can effectively address remaining useful life of integrated modular avionics.

In this paper, an online sequential learning algorithm known as online sequential extreme learning machine (OS ELM) is applied to simulate the power output of a wind turbine. The OS ELM is used both in 1-by-1 and chunk-bychunk mode and the results are compared with batch learning algorithms, namely Back Propagation (BP) and Extreme Learning Machine (ELM) algorithm. Different activation functions such as Sigmoidal, Sin, Radial Basis Function (RBF) and Hardlim have been used in OS ELM to decide upon most optimal function. It has been found that OS ELM with fixed chunk size of 50-by-50 and sigmoidal activation function with training time of 0.080s, Root Mean Square Error (RMSE) of 1.96%, prediction accuracies on training and test data of 100% and 99.95 % respectively, is best suited for wind power modelling and simulation applications, where the data arrives in a sequential manner.

The uncertainty of demands brings challenges for the private cloud providers, leading to low utilization of resources during periods of low-demand and low quality of service during periods of peak-demand, which has attracted much attention. In this paper, taking account into both uncertainty of demands and budget constraint, we design an online sequential procurement auctions of residual resources, which helps the busy cloud provider make an irrevocable decision about how to purchase resources during period of uncertain peak-demand. The crucial part of the mechanism is the seller accepting-rule based on a value-density threshold which is learned dynamically from the historical information. Given the condition that all the sellers are myopic, we prove that the mechanism is truthful, budget feasible and individual rational. Furthermore, we obtain the competitive ratio of the proposed mechanism when the demands of the BCP are δ-degree balance. Using real data from parallel computing centers, we construct 60 scenarios in six data settings, in which we compare our mechanism with average budget allocation and offline proportional sharing mechanism, the results show that in more than 85% scenarios the proposed mechanism has better performance than allocation with average budget, and it improves more than 20% valuation on average for the buyer, even if we use the estimate value of balance degree δ.

Hematocrit (HCT) is expressed as the percentage of red blood cells in the whole blood, it is one of the most highly affecting factors which influences the glucose measurement by using handheld device. In this paper, we present an approach for applying the regularized online sequential learning to hematocrit estimation. The input is the transduced current curve which is produced by the chemical reaction during glucose measurement. The experimental results shown that the proposed approach is promising.

In this paper, view of the unstable output of a single online sequential learning machine, we propose a selective ensemble algorithm based on glowworm swarm optimization. On the basis of this algorithm, we design an adaptive learning framework of multiple learning machines, which can judge whether to use multiple learning machines for selective ensemble according to the preset threshold. The experimental results show that the proposed approach has higher classification accuracy and generalization performance compared with the basic online sequential extreme learning machine as well as the voting online sequential extreme learning machine.

International audience; A successful implementation of Model Predictive Control (MPC) requires appropriately tuned parameters. In This paper an Artificial Neural Network (ANN) based approach is presented. To build the data learning base of the ANN, we adopted the Particle Swarm Optimisation (PSO) method, and we used an Online Sequential Extreme-Learning-Machine (OS-ELM) algorithm to learn the ANN. The objective of this work is to to ensure robustness of the closed-loop system against neglecting dynamics, disturbances and sensor noises, and also show that good tuning of MPC parameters allows to reach closed-loop stability without using robustification approaches in addition to MPC. The effectiveness of our approach has been emphasized by comparing the obtained performances to Generalized Predictive Control (GPC) approach without robustification approach, and also to a robustified (GPC) using Youla parametrisation in the presence of disturbances.

International audience; A successful implementation of Model Predictive Control (MPC) requires appropriately tuned parameters. This paper presents a novel tuning approach based on Artificial Neural Network (ANN). To build the data learning base of the ANN, we adopted the Particle Swarm Optimisation (PSO) method, and we used the reliable algorithm, Online Sequential Extreme-Learning-Machine (OS-ELM) to learn the ANN. The objective of this work is to show that good tuning of MPC parameters makes it possible to reach closed-loop stability and ensure robustness against disturbances and sensor noises, without using robustification approaches in addition to MPC. The effectiveness of our approach is brought to light by comparing the obtained performances to other MPC tuning approaches without disturbances, and also to a robustified Generalized Predictive Control (GPC) using Youla parametrisation in the presence of disturbances.

Extreme learning machines (ELM) were created to simplify the training phase of single-layer feedforward neural networks, where the input weights are randomly set and the only parameter is the number of neurons in the hidden layer. These networks are also known for one-shot training using Moore–Penrose pseudo-inverse. In this work, we propose Gauss–Seidel extreme learning machine (GS-ELM), an ELM based on Gauss–Seidel iterative method to solve linear equation systems. We performed tests considering databases with different characteristics and analysed its discrimination capabilities and memory consumption in comparison to the canonical ELM and the online sequential ELM. GS-ELM presented similar discrimination capabilities, but consuming significantly less memory, turning possible its application in low-memory systems and embedded solutions.

The main contribution of this paper is to introduce a new iterative training algorithm for restricted Boltzmann machines. The proposed learning path is inspired from online sequential extreme learning machine one of extreme learning machine variants which deals with time accumulated sequences of data with fixed or varied sizes. Recursive least squares rules are integrated for weights adaptation to avoid learning rate tuning and local minimum issues. The proposed approach is compared to one of the well known training algorithms for Boltzmann machines named “contrastive divergence”, in term of time, accuracy and algorithmic complexity under the same conditions. Results strongly encourage the new given rules during data reconstruction.

This paper presents a time-varying discrete-time neural-network-based auto-regressive model with exogenous inputs (NN-ARX), which is continuously updated using the online sequential extreme learning algorithm (OS-ELM). The model exhibited a consistently satisfactory performance when describing the dynamics of the payload as a function of users, from real trac data of two stations in a 4G-LTE network. Some advantages of the proposed NN-ARX model include: (i) it provides more modeling flexibilty than classical nonlinear-regression-approaches, (ii) it has adaptive capabilities to characterize the time-varying-behavior of dierent cells and (iii) it has low computational cost, which makes the model suitable for the implementation of optimization and control strategies to improve the performance of the network.

This chapter aims to investigate the capabilities and usefulness of two new data-driven techniques: optimally pruned extreme learning machine (OPELM) and online sequential extreme learning machine (OSELM) newly applied and compared for predicting daily reference evapotranspiration (ET0) in the Mediterranean region of Algeria. Using large data sets from east to west regions of Algeria, the models were developed using several well-known climatic variables as inputs: daily maximum and minimum air temperatures, wind speed, and relative humidity. The proposed models were compared using several well-known statistical indexes: root mean square error (RMSE), mean absolute error (MAE), and coefficient of correlation (R). The obtained results have shown that all the proposed models present high prediction accuracy and the OPELM models provide better overall performances compared to the OSELM models

Accurate prediction of tumor motion for motion adaptive radiotherapy has been a challenge as respiration-induced motion is non-stationary in nature and often subjected to irregularities. Despite having a plethora of works for predicting this motion, their tracking capabilities are usually prone to large prediction errors due to the time-varying irregularities and intra-trace variabilities. To overcome this, prediction models are re-trained at regular intervals. This solution however demands a trade-off between the re-training interval and prediction accuracy in estimating the future tumor location. This is because re-training with small interval increases the computational requirements whereas a larger interval hampers the prediction performance. To address these issues, a prediction model that relies on random convolution nodes (RCN) governed by local receptive fields (LRFs) is proposed for respiratory motion prediction. The innate nature of LRFs extracts the features that contribute to the local-patterns as well as the non-stationary patterns in recent samples and subsequently learn them using extreme learning machine (ELM) theories. To address the re-training issue, we propose an online sequential learning framework (OS-fRCN) that can update the model parameters at regular intervals. Suitability of the proposed OS-fRCN for respiratory motion prediction is evaluated on 304 respiratory motion traces. Performance analysis conducted at four prediction horizons (in-line with the commercially available radiotherapy systems) demonstrated that the proposed OS-fRCN method requires less computational complexity and yields robust, accurate prediction performance when compared with existing prediction methods. This research was supported by the National Natural Science Foundation of China under No. 81701787, the Natural Science Basic Research Plan in Shaanxi Province of China (2019JQ-138), and 2019GHY112041 Primary RD Program of Shandong Province (Public welfare). The research was also supported in part by the National Research Foundation (NRF) of Korea through the Ministry of Education, Science and Technology under Grant NRF2018R1A6A1A03025109.

Field-programmable gate array (FPGA) has been used as a very effective hardware platform in different research area as it enhances the efficiency of the embedded module. The accessibility of minimized, fast circuitry for a artificial neural networks (ANNs) is the most important and utmost necessity for many critical applications. In this paper, a single layer feed-forward neural network (SLFN) named as online sequential extreme learning machine (OS-ELM) is conferred and realized in digital platform for real-world data classification. The digital employment of OS-ELM supports to form an efficient hardware unit for data classification in real-time as the classifier has high learning speed and promising accuracy. Finally, the digital architecture of OS-ELM is implemented on a Virtex-5 FPGA hardware platform to validate the feasibility, efficacy, and vitality of the proposed classifier in real-time.

An adaptive online sequential extreme learning machine (AOS-ELM) is proposed to predict the frequency-dependent sound pressure level (SPL) data of various compartments onboard of the offshore platform. With limited samples and sequential data for training during the initial design stage, conventional neural network training gives significant errors and long computing time when it maps the available inputs to sound pressure level for the entire offshore platform. By using AOS-ELM, it allows a gradual increase in the dataset that is hard to obtain during the initial design stage of the offshore platform. The SPL prediction using AOS-ELM has improved with smaller root mean squared error in testing and shorter training time as compared with other types of ELM based learnings and other gradient based methods in neural network training.

Neuro-controllers that are applicable to a wider range of dynamical systems are the focus of this paper. The proposed controllers are inversion-based wherein the inverse of the dynamical system is modelled using a feedforward neural network with a single hidden layer and trained with the online sequential learning algorithm. We demonstrate that the proposed controller is applicable to both single- and twolink robot manipulators, and is able to handle flexible joints and unmodelled dynamics.

The objective of classification in high dimensional biomedical data is to map an input feature space to a predefined class labels with higher classification accuracy and less computational time. Although various machine learning techniques have been developed till now, still it is a challenging issue among researchers to develop accurate classifier for better diagnosis of various diseases. Therefore, in this study a new classifier named as adaptive fruit fly optimization (AFFO) based online sequential extreme learning machine (OSELM) is presented to classify biomedical data like Indian diabetes, Parkinson and Indian liver patient available in UCI repository. The proposed model performance is also compared with various models like AFFO-ELM and OSELM. The performances of these models are validated using some performance measures such as G-mean, sensitivity, F-Score, accuracy, specificity, and precision. The results confirm that AFFO-OSELM performance outperforms over other models used in this study.

Prediction models are essential in dam crack behavior identification. Prototype monitoring data arrive sequentially in dam safety monitoring. Given such characteristic, sequential learning algorithms are preferred over batch learning algorithms as they do not require retraining whenever new data are received. A new methodology using the genetic optimized online sequential extreme learning machine and bootstrap confidence intervals is proposed as a practical tool for identifying concrete dam crack behavior. First, online sequential extreme learning machine is adopted to build an online prediction model of crack behavior. The characteristic vector of crack behavior, which is taken as the online sequential extreme learning machine input, is extracted by the statistical model. A genetic algorithm is introduced to optimize the input weights and biases of online sequential extreme learning machine. Second, the BC a method is proposed to produce confidence intervals based on the improved online sequential extreme learning machine prediction. The improved online sequential extreme learning machine for identifying crack behavior is then built. Third, the crack behavior of an actual concrete dam is taken as an example. The capability of the built model for predicting dam crack opening is evaluated. The comparative results demonstrate that the improved online sequential extreme learning machine can provide highly accurate forecasts and reasonably identify crack behavior.

In this paper, a novel approach was presented to vibration analysis and identification of breathing cracks in Timoshenko beam under single or multiple moving mass. Dynamic strain energies (...