Nonlinear optimal control of stochastic recurrent neural networks with multiple time delays

Ziqian Liu, Qunjing Wang, Nirwan Ansari, Henri Schurz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a theoretical design of how a nonlinear optimal control is achieved for multiple time-delayed recurrent neural networks under the influence of random perturbations. Our objective is to build stabilizing control laws to accomplish global asymptotic stability in probability as well as optimality with respect to disturbance attenuation for stochastic delayed recurrent neural networks. The formulation of the nonlinear optimal control is developed by using stochastic Lyapunov technique and solving a Hamilton-Jacobi-Bellman (HJB) equation indirectly. To illustrate the analytical results, a numerical example is given to demonstrate the effectiveness of the proposed approach.

Original languageEnglish (US)
Title of host publication2012 American Control Conference, ACC 2012
Pages6424-6429
Number of pages6
StatePublished - Nov 26 2012
Event2012 American Control Conference, ACC 2012 - Montreal, QC, Canada
Duration: Jun 27 2012Jun 29 2012

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

Other

Other2012 American Control Conference, ACC 2012
CountryCanada
CityMontreal, QC
Period6/27/126/29/12

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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