Stability Analysis for Delayed Neural Networks Considering Both Conservativeness and Complexity

This paper investigates delay-dependent stability for continuous neural networks with a time-varying delay. This paper aims at deriving a new stability criterion, considering tradeoff between conservativeness and calculation complexity. A new Lyapunov-Krasovskii functional with simple augmented term...

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Published in	IEEE transaction on neural networks and learning systems Vol. 27; no. 7; pp. 1486 - 1501
Main Authors	Zhang, Chuan-Ke, He, Yong, Jiang, Lin, Wu, Min
Format	Journal Article
Language	English
Published	United States IEEE 01.07.2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects	Biological neural networks Calculation complexity Complexity Complexity theory conservativeness Criteria Delay delay-dependent stability delayed neural networks (DNNs) Delays Derivatives Guidelines Lyapunov-Krasovskii functional (LKF) Mathematical analysis Neural networks Numerical stability Stability Stability criteria Symmetric matrices delay-dependent stability conservativeness Lyapunov–Krasovskii functional (LKF) Calculation complexity delayed neural networks (DNNs)
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Abstract	This paper investigates delay-dependent stability for continuous neural networks with a time-varying delay. This paper aims at deriving a new stability criterion, considering tradeoff between conservativeness and calculation complexity. A new Lyapunov-Krasovskii functional with simple augmented terms and delay-dependent terms is constructed, and its derivative is estimated by several techniques, including free-weighting matrix and inequality estimation methods. Then, the influence of the techniques used on the conservativeness and the complexity is analyzed one by one. Moreover, useful guidelines for improving criterion and future work are briefly discussed. Finally, the advantages of the proposed criterion compared with the existing ones are verified based on three numerical examples.
AbstractList	This paper investigates delay-dependent stability for continuous neural networks with a time-varying delay. This paper aims at deriving a new stability criterion, considering tradeoff between conservativeness and calculation complexity. A new Lyapunov-Krasovskii functional with simple augmented terms and delay-dependent terms is constructed, and its derivative is estimated by several techniques, including free-weighting matrix and inequality estimation methods. Then, the influence of the techniques used on the conservativeness and the complexity is analyzed one by one. Moreover, useful guidelines for improving criterion and future work are briefly discussed. Finally, the advantages of the proposed criterion compared with the existing ones are verified based on three numerical examples. This paper investigates delay-dependent stability for continuous neural networks with a time-varying delay. This paper aims at deriving a new stability criterion, considering tradeoff between conservativeness and calculation complexity. A new Lyapunov-Krasovskii functional with simple augmented terms and delay-dependent terms is constructed, and its derivative is estimated by several techniques, including free-weighting matrix and inequality estimation methods. Then, the influence of the techniques used on the conservativeness and the complexity is analyzed one by one. Moreover, useful guidelines for improving criterion and future work are briefly discussed. Finally, the advantages of the proposed criterion compared with the existing ones are verified based on three numerical examples.This paper investigates delay-dependent stability for continuous neural networks with a time-varying delay. This paper aims at deriving a new stability criterion, considering tradeoff between conservativeness and calculation complexity. A new Lyapunov-Krasovskii functional with simple augmented terms and delay-dependent terms is constructed, and its derivative is estimated by several techniques, including free-weighting matrix and inequality estimation methods. Then, the influence of the techniques used on the conservativeness and the complexity is analyzed one by one. Moreover, useful guidelines for improving criterion and future work are briefly discussed. Finally, the advantages of the proposed criterion compared with the existing ones are verified based on three numerical examples.
Author	Min Wu Chuan-Ke Zhang Lin Jiang Yong He
Author_xml	– sequence: 1 givenname: Chuan-Ke surname: Zhang fullname: Zhang, Chuan-Ke – sequence: 2 givenname: Yong surname: He fullname: He, Yong – sequence: 3 givenname: Lin surname: Jiang fullname: Jiang, Lin – sequence: 4 givenname: Min surname: Wu fullname: Wu, Min
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/26208366$$D View this record in MEDLINE/PubMed
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Snippet	This paper investigates delay-dependent stability for continuous neural networks with a time-varying delay. This paper aims at deriving a new stability...
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SubjectTerms	Biological neural networks Calculation complexity Complexity Complexity theory conservativeness Criteria Delay delay-dependent stability delayed neural networks (DNNs) Delays Derivatives Guidelines Lyapunov-Krasovskii functional (LKF) Mathematical analysis Neural networks Numerical stability Stability Stability criteria Symmetric matrices
Title	Stability Analysis for Delayed Neural Networks Considering Both Conservativeness and Complexity
URI	https://ieeexplore.ieee.org/document/7161350 https://www.ncbi.nlm.nih.gov/pubmed/26208366 https://www.proquest.com/docview/1798038758 https://www.proquest.com/docview/1798997961 https://www.proquest.com/docview/1808613804 https://www.proquest.com/docview/1825521236
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