Discrete Dynamics in Nature and Society
Volume 2010 (2010), Article ID 415895, 14 pages
doi:10.1155/2010/415895
Research Article

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

Choon Ki Ahn

Department of Automotive Engineering, Seoul National University of Science and Technology, 172 Gongneung 2-dong, Nowon-gu, Seoul 139-743, Republic of Korea

Received 8 September 2010; Accepted 16 December 2010

Academic Editor: Chang-hong Wang

Copyright © 2010 Choon Ki Ahn. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.