Adaptive Synchronization Control and Parameters Identification for Chaotic Fractional Neural Networks with Time-Varying Delays

Sun, Yumei; Liu, Yihong

doi:10.1007/s11063-021-10517-7

Cited by 13 publications

(7 citation statements)

References 37 publications

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“…Moreover, the fractional-order bidirectional associative memory [189] in the neural networks is a recent hot topic. However, synchronization and control of fractionalorder chaotic neural networks are widely investigated [190][191][192] where the coupling controller is most used method and different spatiotemporal patterns can be observed with different coupling methods. [182] Chimera states which are related to the actual neuronal activities are found in such as fractional-order chaotic neural networks.…”

Section: Fractional-order Chaotic Neural Networkmentioning

confidence: 99%

Solutions and memory effect of fractional-order chaotic system: A review

He¹,

Wang²,

Sun³

2022

Chinese Phys. B

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Fractional calculus is a 300 years topic, which has been introduced to real physics systems modeling and engineering applications. In the last few decades, fractional-order nonlinear chaotic systems have been widely investigated. Firstly, the most used methods to solve fractional-order chaotic systems are reviewed. Characteristics and memory effect in those method are summarized. Then we discussed the memory effect in the fractional-order chaotic systems through the fractional-order calculus and numerical solution algorithms. It shows that the integer-order derivative has full memory effect, while the fractional-order derivative has nonideal memory effect due to the kernel function. Memory lose and short memory are discussed. Finally, applications of the fractional-order chaotic systems regarding the memory effects are investigated. The work summarized in this manuscript provides reference value for the applied scientists and engineering community of fractional-order nonlinear chaotic systems.

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Section: Fractional-order Chaotic Neural Networkmentioning

confidence: 99%

Solutions and memory effect of fractional-order chaotic system: A review

He¹,

Wang²,

Sun³

2022

Chinese Phys. B

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“…Remark 3. Many previous studies mainly focused on fractional-order neural networks with single fractional-order derivative of the states [17]- [30]. However, it is important to introduce an inertia term, which is considered as a powerful tool to generate complex bifurcation behavior and chaos.…”

Section: B Problem Formulationmentioning

confidence: 99%

“…[17]- [24]. With the development of theory on fractional-order, the study of synchronization control for fractional-order neural networks (NNS) has received more and more attention and some interesting results have been obtained, such as asymptotical synchronization [25], exponential synchronization [26], finite-time synchronization [27], fixed-time synchronization [28], robust synchronization [29], and adaptive synchronization [30].…”

Section: Introductionmentioning

confidence: 99%

Stability Analysis and Synchronization Control of Fractional-Order Inertial Neural Networks With Time-Varying Delay

2022

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This paper mainly investigates the stability analysis and synchronization control of a fractional-order time-varying delay inertial neural network. Firstly, a time-varying delay inertial neural network model is established, which is easy to implement in engineering applications. Secondly, based on the properties of the Caputo fractional derivative and the proposed lemma, the original inertial system is transferred into conventional system through the proper variable substitution, and a synchronous control strategy for the time varying delay inertial network is then established. In addition, the stability conditions of a class of Caputo fractional-order time-delay inertial neural networks are given. Finally, three simulation examples are given to verify the rationality and effectiveness of the method proposed in this paper.

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“…The adaptive filter can effectively overcome the interference caused by model uncertainty. Chen [9] proposed a method to convert the identification of time-varying parameters into the identification of constant parameter. In this method, the time-varying parameters curves are approximated by multiple polyline segments, and the identification process is completed by the least squares technique.…”

Section: Introductionmentioning

confidence: 99%

Time-Varying Parameters Estimation with Adaptive Neural Network EKF for Missile-Dual Control System

Yuan,

Zhou,

et al. 2024

J. of Syst. Eng. Electron.

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In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory (LSTM) neural network is nested into the extended Kalman filter (EKF) to modify the Kalman gain such that the filtering performance is improved in the presence of large model uncertainties. To avoid the unstable network output caused by the abrupt changes of system states, an adaptive correction factor is introduced to correct the network output online. In the process of training the network, a multi-gradient descent learning mode is proposed to better fit the internal state of the system, and a rolling training is used to implement an online prediction logic. Based on the Lyapunov second method, we discuss the stability of the system, the result shows that when the training error of neural network is sufficiently small, the system is asymptotically stable. With its application to the estimation of time-varying parameters of a missile dual control system, the LSTM-EKF shows better filtering performance than the EKF and adaptive EKF (AEKF) when there exist large uncertainties in the system model.

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Adaptive Synchronization Control and Parameters Identification for Chaotic Fractional Neural Networks with Time-Varying Delays

Cited by 13 publications

References 37 publications

Solutions and memory effect of fractional-order chaotic system: A review

Solutions and memory effect of fractional-order chaotic system: A review

Stability Analysis and Synchronization Control of Fractional-Order Inertial Neural Networks With Time-Varying Delay

Time-Varying Parameters Estimation with Adaptive Neural Network EKF for Missile-Dual Control System

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