A New Robust Training Law for Dynamic Neural Networks with External Disturbance: An LMI Approach | Zendy

Choon Ki Ahn | Zendy

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A New Robust Training Law for Dynamic Neural Networks with External Disturbance: An LMI Approach

Author(s) -

Choon Ki Ahn

Publication year - 2010

Publication title -

discrete dynamics in nature and society

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.264

H-Index - 39

eISSN - 1607-887X

pISSN - 1026-0226

DOI - 10.1155/2010/415895

Subject(s) - disturbance (geology) , control theory (sociology) , linear matrix inequality , computer science , artificial neural network , stability (learning theory) , law , exponential stability , mathematics , mathematical optimization , control (management) , artificial intelligence , nonlinear system , physics , machine learning , paleontology , quantum mechanics , political science , biology

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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