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Neutral-type of delayed inertial neural networks and their stability analysis using the LMI Approach

Version 2 2024-06-06, 08:08
Version 1 2017-04-05, 11:41
journal contribution
posted on 2024-06-06, 08:08 authored by S Lakshmanan, Chee Peng LimChee Peng Lim, M Prakash, S Nahavandi, P Balasubramaniam
A theoretical investigation of neutral-type of delayed inertial neural networks using the Lyapunov stability theory and Linear Matrix Inequality (LMI) approach is presented. Based on a suitable variable transformation, an inertial neural network consisting of second-order differential equations can be converted into a first-order differential model. The sufficient conditions of the delayed inertial neural network are derived by constructing suitable Lyapunov functional candidates, introducing new free weighting matrices, and utilizing the Writinger integral inequality. Through the LMI solution, we analyse the global asymptotic stability condition of the resulting delayed inertial neural network. Simulation examples are presented to demonstrate the effectiveness of the derived analytical results.

History

Journal

Neurocomputing

Volume

230

Pagination

243-250

Location

Amsterdam, The Netherlands

ISSN

0925-2312

eISSN

1872-8286

Language

English

Publication classification

C Journal article, C1 Refereed article in a scholarly journal

Copyright notice

2016, Elsevier

Publisher

ELSEVIER SCIENCE BV