文章基本信息

标题：A New Robust Training Law for Dynamic Neural Networks with External Disturbance: An LMI Approach
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作者：Choon Ki Ahn
期刊名称：Discrete Dynamics in Nature and Society
印刷版ISSN：1026-0226
电子版ISSN：1607-887X
出版年度：2010
卷号：2010
DOI：10.1155/2010/415895
出版社：Hindawi Publishing Corporation
摘要：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.