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Pinning Control for Synchronization of Drive-Response Memristive Neural Networks with Nonidentical Parameters

In this paper, the asymptotic synchronization for drive-response memristive neural networks(MNNs) with nonidentical parameters is investigated. Parameter inconformity is ubiquitous between drive and response systems due to environmental or internal influence. However, the majority of previous result...

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Main Authors:	Li, Yueheng, Luo, Biao, Liu, Derong, Dong, Zhe, Yang, Zhanyu
Format:	Conference Proceeding
Language:	English
Subjects:	asymptotic synchronization Automation Biological neural networks Biological system modeling Delays drive-response systems Memristive neural networks Memristors nonidentical parameters pinning control Synchronization
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creator	Li, Yueheng Luo, Biao Liu, Derong Dong, Zhe Yang, Zhanyu
description	In this paper, the asymptotic synchronization for drive-response memristive neural networks(MNNs) with nonidentical parameters is investigated. Parameter inconformity is ubiquitous between drive and response systems due to environmental or internal influence. However, the majority of previous results were based on the well-matched MNNs. Thus, it is meaningful to study the synchronization problem of MNNs with nonidentical parameters. First, coefficient mismatches are dealt within the framework of set-valued maps and differential inclusions. Furthermore, in order to reduce the control cost, a pinning control strategy is adopted to drive two nonidentical MNNs to achieve asymptotic synchronization. And the sufficient stability conditions are given based on Lyapunov functional method. Finally, the effectiveness of proposed pinning controller is verified by a numerical example.
doi_str_mv	10.1109/IJCNN.2019.8851745
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subjects	asymptotic synchronization Automation Biological neural networks Biological system modeling Delays drive-response systems Memristive neural networks Memristors nonidentical parameters pinning control Synchronization
title	Pinning Control for Synchronization of Drive-Response Memristive Neural Networks with Nonidentical Parameters
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