Nonlinear Dynamics

, Volume 65, Issue 4, pp 413–419

Robust stability of recurrent neural networks with ISS learning algorithm

Original Paper

DOI: 10.1007/s11071-010-9901-5

Cite this article as:
Ahn, C.K. Nonlinear Dyn (2011) 65: 413. doi:10.1007/s11071-010-9901-5

Abstract

In this paper, an input-to-state stability (ISS) approach is used to derive a new robust weight learning algorithm for dynamic neural networks with external disturbance. Based on linear matrix inequality (LMI) formulation, the ISS learning algorithm is presented to not only guarantee exponential stability but also reduce the effect of an external disturbance. It is shown that the design of the ISS learning algorithm can be achieved by solving LMI, which can be easily facilitated by using some standard numerical packages. A numerical example is presented to demonstrate the validity of the proposed learning algorithm.

Keywords

Input-to-state stability (ISS) approach Weight learning algorithm Dynamic neural networks Linear matrix inequality (LMI) 

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Department of Automotive EngineeringSeoul National University of Science & TechnologySeoulSouth Korea

Personalised recommendations