Direct adaptive control for nonlinear systems using a TSK fuzzy echo state network based on fractional-order learning algorithm

Mahmoud, Tarek A.; Abdo, Mohamed I.; Elsheikh, Emad A.; Elshenawy, Lamiaa M.

doi:10.1016/j.jfranklin.2021.09.015

Cited by 16 publications

(8 citation statements)

References 66 publications

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“…Therefore, the output and residual error of the SCFS-IFRs (11) on dataset {X, Y} can be calculated as…”

Section: General Structure Of Scfs-ifrsmentioning

confidence: 99%

“…Suppose that there is SCFS-IFRs S L−1 with L − 1 fuzzy rules of the form (11). The output and residual error of S L−1 on dataset {X, Y} are expressed in the form…”

Section: Adaptive Incremental Learning For Scfs-ifrsmentioning

confidence: 99%

“…The most commonly used fuzzy system in the neural fuzzy model is the Takagi-Sugeno-Kang (TSK), which has been widely applied in many fields, such as regression [8], classification [9], model approximation [10], adaptive control [11], business [12], etc. Neuralfuzzy systems adopt the form of a neural network to represent the fuzzy system, and the training algorithm in the neural network is used to realize the extraction of fuzzy rules and the establishment of the fuzzy system without tedious manual intervention.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic Configuration Based Fuzzy Inference System with Interpretable Fuzzy Rules and Intelligence Search Process

Zhou

Bao

2023

Mathematics

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In this paper, a stochastic configuration based fuzzy inference system with interpretable fuzzy rules (SCFS-IFRs) is proposed to improve the interpretability and performance of the fuzzy inference system and determine autonomously an appropriate model structure. The proposed SCFS-IFR first accomplishes a fuzzy system through interpretable linguistic fuzzy rules (ILFRs), which endows the system with clear semantic interpretability. Meanwhile, using an incremental learning method based on stochastic configuration, the appropriate architecture of the system is determined by incremental generation of ILFRs under a supervision mechanism. In addition, the particle swarm optimization (PSO) algorithm, an intelligence search technique, is used in the incremental learning process of ILFRs to obtain better random parameters and improve approximation accuracy. The performance of SCFS-IFRs is verified by regression and classification benchmark datasets. Regression experiments show that the proposed SCFS-IFRs perform best on 10 of the 20 data sets, statistically significantly outperforming the other eight state-of-the-art algorithms. Classification experiments show that, compared with the other six fuzzy classifiers, SCFS-IFRs achieve higher classification accuracy and better interpretation with fewer rules.

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“…Therefore, the output and residual error of the SCFS-IFRs (11) on dataset {X, Y} can be calculated as…”

Section: General Structure Of Scfs-ifrsmentioning

confidence: 99%

“…Suppose that there is SCFS-IFRs S L−1 with L − 1 fuzzy rules of the form (11). The output and residual error of S L−1 on dataset {X, Y} are expressed in the form…”

Section: Adaptive Incremental Learning For Scfs-ifrsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Stochastic Configuration Based Fuzzy Inference System with Interpretable Fuzzy Rules and Intelligence Search Process

Zhou

Bao

2023

Mathematics

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“…The architecture of ESNs is characterized by their straightforward design, while the training procedure is known for its efficiency in terms of speed. ESNs have been applied successfully to a wide range of domains, including nonlinear modeling [5], pattern recognition [6], fuzzy nonlinear control [7,8], time series prediction [9][10][11][12], and so on.…”

Section: Introductionmentioning

confidence: 99%

A Multireservoir Echo State Network Combined with Olfactory Feelings Structure

Lun,

Wang,

Cai

et al. 2023

Electronics

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As a special form of recurrent neural network (RNN), echo state networks (ESNs) have achieved good results in nonlinear system modeling, fuzzy nonlinear control, time series prediction, and so on. However, the traditional single-reservoir ESN topology limits the prediction ability of the network. In this paper, we design a multireservoir olfactory feelings echo state network (OFESN) inspired by the structure of the Drosophila olfactory bulb, which provides a new connection mode. The connection between subreservoirs is transformed into the connection between each autonomous neuron, the neurons in each subreservoir are sparsely connected, and the neurons in different subreservoirs cannot communicate with each other. The OFESN greatly simplifies the coupling connections between neurons in different libraries, reduces information redundancy, and improves the running speed of the network. The findings from the simulation demonstrate that the OFESN model, as introduced in this study, enhances the capacity to approximate sine superposition function and the Mackey–Glass system when combined. Additionally, this model exhibits improved prediction accuracy by 98% in some cases and reduced fluctuations in prediction errors.

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“…On the other side, the fractional calculus was efficiently incorporated into the field of neural networks. For instant, FO-neural networks have been conducted for time series prediction [77], nonlinear system modeling and control [1,13,36]. Besides, a new fractional derivative operator with sigmoid function as the kernel was proposed in [33].…”

Section: Introductionmentioning

confidence: 99%

Optimal fractional-order PID controller based on fractional-order actor-critic algorithm

Shalaby

El-Hossainy²,

Abozalam

et al. 2022

Neural Comput & Applic

Self Cite

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In this paper, an online optimization approach of a fractional-order PID controller based on a fractional-order actor-critic algorithm (FOPID-FOAC) is proposed. The proposed FOPID-FOAC scheme exploits the advantages of the FOPID controller and FOAC approaches to improve the performance of nonlinear systems. The proposed FOAC is built by developing a FO-based learning approach for the actor-critic neural network with adaptive learning rates. Moreover, a FO rectified linear unit (RLU) is introduced to enable the AC neural network to define and optimize its own activation function. By the means of the Lyapunov theorem, the convergence and the stability analysis of the proposed algorithm are investigated. The FO operators for the FOAC learning algorithm are obtained using the gray wolf optimization (GWO) algorithm. The effectiveness of the proposed approach is proven by extensive simulations based on the tracking problem of the two degrees of freedom (2-DOF) helicopter system and the stabilization issue of the inverted pendulum (IP) system. Moreover, the performance of the proposed algorithm is compared against optimized FOPID control approaches in different system conditions, namely when the system is subjected to parameter uncertainties and external disturbances. The performance comparison is conducted in terms of two types of performance indices, the error performance indices, and the time response performance indices. The first one includes the integral absolute error (IAE), and the integral squared error (ISE), whereas the second type involves the rising time, the maximum overshoot (Max. OS), and the settling time. The simulation results explicitly indicate the high effectiveness of the proposed FOPID-FOAC controller in terms of the two types of performance measurements under different scenarios compared with the other control algorithms.

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Direct adaptive control for nonlinear systems using a TSK fuzzy echo state network based on fractional-order learning algorithm

Cited by 16 publications

References 66 publications

Stochastic Configuration Based Fuzzy Inference System with Interpretable Fuzzy Rules and Intelligence Search Process

Stochastic Configuration Based Fuzzy Inference System with Interpretable Fuzzy Rules and Intelligence Search Process

A Multireservoir Echo State Network Combined with Olfactory Feelings Structure

Optimal fractional-order PID controller based on fractional-order actor-critic algorithm

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