Extended dissipative analysis for neural networks with time-varying delays

Tae H. Lee; Myeong Jin Park; Ju H. Park; Oh Min Kwon; Sang Moon Lee

doi:10.1109/TNNLS.2013.2296514

Extended dissipative analysis for neural networks with time-varying delays

Tae H. Lee, Myeong Jin Park, Ju H. Park, Oh Min Kwon, Sang Moon Lee

School of Electronics Engineering

Research output: Contribution to journal › Article › peer-review

175 Scopus citations

Abstract

In this brief, an extended dissipativity analysis was conducted for a neural network with time-varying delays. The concept of the extended dissipativity can be used to solve for the H_∞, L₂ - L_∞, passive, and dissipative performance by adjusting the weighting matrices in a new performance index. In addition, the activation function dividing method is modified by introducing a tuning parameter. Examples are provided to show the effectiveness and less conservatism of the proposed method.

Original language	English
Article number	6712166
Pages (from-to)	1936-1941
Number of pages	6
Journal	IEEE Transactions on Neural Networks and Learning Systems
Volume	25
Issue number	10
DOIs	https://doi.org/10.1109/TNNLS.2013.2296514
State	Published - 1 Oct 2014

Keywords

Dissipativity
neural networks
passivity
stability

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10.1109/TNNLS.2013.2296514

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Extended dissipative analysis for neural networks with time-varying delays

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