Neural network H∞ chaos synchronization

Choon Ki Ahn

doi:10.1007/s11071-009-9596-7

Neural network H∞ chaos synchronization

Choon Ki Ahn

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

31 Citations (Scopus)

Abstract

This paper proposes a new neural network H∞ synchronization (NNHS) scheme for unknown chaotic systems. In the proposed framework, a dynamic neural network is constructed as an alternative to approximate the chaotic system. Based on this neural network and linear matrix inequality (LMI) formulation, the NNHS controller and the learning law are presented to reduce the effect of disturbance to an H∞ norm constraint. It is shown that finding the NNHS controller and the learning law can be transformed into the LMI problem and solved using the convex optimization method. A numerical example is presented to demonstrate the validity of the proposed NNHS scheme.

Original language	English
Pages (from-to)	295-302
Number of pages	8
Journal	Nonlinear Dynamics
Volume	60
Issue number	3
DOIs	https://doi.org/10.1007/s11071-009-9596-7
Publication status	Published - 2010 May
Externally published	Yes

Bibliographical note

Funding Information:
This paper was supported by Wonkwang University in 2009.

Keywords

Dynamic neural networks
H∞ synchronization
Linear matrix inequality (LMI)
Unknown chaotic systems
Weight learning law

ASJC Scopus subject areas

Control and Systems Engineering
Aerospace Engineering
Ocean Engineering
Mechanical Engineering
Applied Mathematics
Electrical and Electronic Engineering

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10.1007/s11071-009-9596-7

Cite this

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title = "Neural network H∞ chaos synchronization",

abstract = "This paper proposes a new neural network H∞ synchronization (NNHS) scheme for unknown chaotic systems. In the proposed framework, a dynamic neural network is constructed as an alternative to approximate the chaotic system. Based on this neural network and linear matrix inequality (LMI) formulation, the NNHS controller and the learning law are presented to reduce the effect of disturbance to an H∞ norm constraint. It is shown that finding the NNHS controller and the learning law can be transformed into the LMI problem and solved using the convex optimization method. A numerical example is presented to demonstrate the validity of the proposed NNHS scheme.",

keywords = "Dynamic neural networks, H∞ synchronization, Linear matrix inequality (LMI), Unknown chaotic systems, Weight learning law",

author = "Ahn, {Choon Ki}",

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AU - Ahn, Choon Ki

N1 - Funding Information: This paper was supported by Wonkwang University in 2009.

PY - 2010/5

Y1 - 2010/5

N2 - This paper proposes a new neural network H∞ synchronization (NNHS) scheme for unknown chaotic systems. In the proposed framework, a dynamic neural network is constructed as an alternative to approximate the chaotic system. Based on this neural network and linear matrix inequality (LMI) formulation, the NNHS controller and the learning law are presented to reduce the effect of disturbance to an H∞ norm constraint. It is shown that finding the NNHS controller and the learning law can be transformed into the LMI problem and solved using the convex optimization method. A numerical example is presented to demonstrate the validity of the proposed NNHS scheme.

AB - This paper proposes a new neural network H∞ synchronization (NNHS) scheme for unknown chaotic systems. In the proposed framework, a dynamic neural network is constructed as an alternative to approximate the chaotic system. Based on this neural network and linear matrix inequality (LMI) formulation, the NNHS controller and the learning law are presented to reduce the effect of disturbance to an H∞ norm constraint. It is shown that finding the NNHS controller and the learning law can be transformed into the LMI problem and solved using the convex optimization method. A numerical example is presented to demonstrate the validity of the proposed NNHS scheme.

KW - Dynamic neural networks

KW - H∞ synchronization

KW - Linear matrix inequality (LMI)

KW - Unknown chaotic systems

KW - Weight learning law

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EP - 302

JO - Nonlinear Dynamics

JF - Nonlinear Dynamics

IS - 3

ER -

Neural network H∞ chaos synchronization

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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