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Neural Computing and Applications
Published in: Neural Computing and Applications 1/2022

31-07-2021 | Original Article

Inverse optimal synchronization control of competitive neural networks with constant time delays

Authors: Xiaomin Liu, Chunyu Yang, Song Zhu

Published in: Neural Computing and Applications | Issue 1/2022

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Abstract

Competitive neural networks (CNNs) are a class of two-time-scale neural networks which can simultaneously represent fast neural activity and slow changes in synapses. In this paper, by means of the drive-response idea and inverse optimality techniques, the optimal synchronization control of two CNNs with constant time delays is solved by considering the inverse optimal synchronization control of the error system. Considering the coupling relationship between fast and slow dynamics of the error system, the control Lyapunov function (CLF) is constructed first. Then, based on the CLF, a state feedback inverse optimal synchronization controller design method is proposed to synchronize two CNNs and minimize a meaningful performance functional while avoiding solving the Hamilton–Jacobi–Bellman (HJB) equation. The designed controller is linear and easy to implement. Finally, the feasibility and superiority of the presented method is illustrated by an example.
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Metadata
Title
Inverse optimal synchronization control of competitive neural networks with constant time delays
Authors
Xiaomin Liu
Chunyu Yang
Song Zhu
Publication date
31-07-2021
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 1/2022
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-021-06358-z

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