A new robust training law for dynamic neural networks with external disturbance: An LMI approach

Choon Ki Ahn

doi:10.1155/2010/415895

A new robust training law for dynamic neural networks with external disturbance: An LMI approach

Choon Ki Ahn

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

2 Citations (Scopus)

Abstract

A new robust training law, which is called an input/output-to-state stable training law (IOSSTL), is proposed for dynamic neural networks with external disturbance. Based on linear matrix inequality (LMI) formulation, the IOSSTL is presented to not only guarantee exponential stability but also reduce the effect of an external disturbance. It is shown that the IOSSTL can be obtained by solving the LMI, which can be easily facilitated by using some standard numerical packages. Numerical examples are presented to demonstrate the validity of the proposed IOSSTL.

Original language	English
Article number	415895
Journal	Discrete Dynamics in Nature and Society
Volume	2010
DOIs	https://doi.org/10.1155/2010/415895
Publication status	Published - 2010
Externally published	Yes

ASJC Scopus subject areas

Modelling and Simulation

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title = "A new robust training law for dynamic neural networks with external disturbance: An LMI approach",

abstract = "A new robust training law, which is called an input/output-to-state stable training law (IOSSTL), is proposed for dynamic neural networks with external disturbance. Based on linear matrix inequality (LMI) formulation, the IOSSTL is presented to not only guarantee exponential stability but also reduce the effect of an external disturbance. It is shown that the IOSSTL can be obtained by solving the LMI, which can be easily facilitated by using some standard numerical packages. Numerical examples are presented to demonstrate the validity of the proposed IOSSTL.",

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AB - A new robust training law, which is called an input/output-to-state stable training law (IOSSTL), is proposed for dynamic neural networks with external disturbance. Based on linear matrix inequality (LMI) formulation, the IOSSTL is presented to not only guarantee exponential stability but also reduce the effect of an external disturbance. It is shown that the IOSSTL can be obtained by solving the LMI, which can be easily facilitated by using some standard numerical packages. Numerical examples are presented to demonstrate the validity of the proposed IOSSTL.

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A new robust training law for dynamic neural networks with external disturbance: An LMI approach

Abstract

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