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

Ziqian Liu; Qunjing Wang; Nirwan Ansari; Henri Schurz

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

Ziqian Liu, Qunjing Wang, Nirwan Ansari, Henri Schurz

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	2012 American Control Conference, ACC 2012
Pages	6424-6429
Number of pages	6
State	Published - 2012
Event	2012 American Control Conference, ACC 2012 - Montreal, QC, Canada Duration: Jun 27 2012 → Jun 29 2012

Publication series

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

Other

Other	2012 American Control Conference, ACC 2012
Country/Territory	Canada
City	Montreal, QC
Period	6/27/12 → 6/29/12

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering

Cite this

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title = "Nonlinear optimal control of stochastic recurrent neural networks with multiple time delays",

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.",

author = "Ziqian Liu and Qunjing Wang and Nirwan Ansari and Henri Schurz",

year = "2012",

language = "English (US)",

isbn = "9781457710957",

series = "Proceedings of the American Control Conference",

pages = "6424--6429",

booktitle = "2012 American Control Conference, ACC 2012",

note = "2012 American Control Conference, ACC 2012 ; Conference date: 27-06-2012 Through 29-06-2012",

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TY - GEN

T1 - Nonlinear optimal control of stochastic recurrent neural networks with multiple time delays

AU - Liu, Ziqian

AU - Wang, Qunjing

AU - Ansari, Nirwan

AU - Schurz, Henri

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84869380830&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84869380830&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84869380830

SN - 9781457710957

T3 - Proceedings of the American Control Conference

SP - 6424

EP - 6429

BT - 2012 American Control Conference, ACC 2012

T2 - 2012 American Control Conference, ACC 2012

Y2 - 27 June 2012 through 29 June 2012

ER -

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

Abstract

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All Science Journal Classification (ASJC) codes

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