Global Exponential Stability of Recurrent Neural Networks with Time-Dependent Switching Dynamics Conference Paper

abstract

• In this paper, the switching dynamics of recurrent neural networks are studied. Sufficient conditions on global exponential stability with an arbitrary switching law or a dwell time switching law and the estimates of Lyapunov exponent are obtained. The obtained results can be used to analyze and synthesize a family of continuous-time configurations with the switching between the configurations. Specially, the obtained results are new and efficacious for the switching between the stable and unstable configurations. Finally, simulation results are discussed to illustrate the theoretical results. 2009 Springer Berlin Heidelberg.

authors

• Huang, Tingwen

published proceedings

• ARTIFICIAL NEURAL NETWORKS - ICANN 2009, PT II

author list (cited authors)

• Zeng, Z., Wang, J., & Huang, T.

citation count

• 0

complete list of authors

• Zeng, Zhigang||Wang, Jun||Huang, Tingwen

publication date

• January 2009

publisher

• Springer Nature  Publisher

published in

• Lecture Notes in Artificial Intelligence  Journal

Digital Object Identifier (DOI)

• 10.1007/978-3-642-04277-5_59

International Standard Book Number (ISBN) 10

• 3642042767

International Standard Book Number (ISBN) 13

• 978-3-642-04276-8

start page

• 583

end page

• +

volume

• 5769

issue

• PART 2