New results on stability analysis of delayed recurrent neural networks based on the integral quadratic constraints approach

Min Zheng, Kang Li

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

This paper is concerned with the analysis of the stability of delayed recurrent neural networks. In contrast to the widely used Lyapunov–Krasovskii functional approach, a new method is developed within the integral quadratic constraints framework. To achieve this, several lemmas are first given to propose integral quadratic separators to characterize the original delayed neural network. With these, the network is then reformulated as a special form of feedback-interconnected system by choosing proper integral quadratic constraints. Finally, new stability criteria are established based on the proposed approach. Numerical examples are given to illustrate the effectiveness of the new approach.
Original languageEnglish
Pages (from-to)780-788
Number of pages9
JournalTransactions of the Institute of Measurement and Control
Volume36
Issue number6
Early online date26 Feb 2014
DOIs
Publication statusPublished - Aug 2014

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