Stochastic recurent neural control for trajectory tracking of a gene regulatory network biological system

Author

Pérez, Jose P. ; Gonzalez, Jorge A. ; Pérez, Joel

Author_Institution

Fac. de Cienc. Fisico-Mat., Univ. Autonoma de Nuevo Leon, San Nicolas de los Garza, Mexico

fYear

2009

fDate

5-8 July 2009

Firstpage

256

Lastpage

260

Abstract

In this paper the problem of trajectory tracking by a stochastic recurrent neural network to a gene regulatory network described by a nonlinear dynamic model is studied. Based on the Lyapunov theory is obtained a control law of that achieves the global asymptotic stability of the tracking error.

Keywords

Lyapunov methods; asymptotic stability; biocontrol; neurocontrollers; nonlinear control systems; position control; recurrent neural nets; stochastic systems; Lyapunov theory; gene regulatory network biological system; global asymptotic stability; nonlinear dynamic model; stochastic recurrent neural control; trajectory tracking; Asymptotic stability; Biological control systems; Biological system modeling; Biological systems; Control systems; Nonlinear dynamical systems; Recurrent neural networks; Stochastic processes; Stochastic systems; Trajectory; Trajectory tracking; gene network; stochastic Lyapunov analysis; stochastic recurent neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics, 2009. ISIE 2009. IEEE International Symposium on

Conference_Location

Seoul

Print_ISBN

978-1-4244-4347-5

Electronic_ISBN

978-1-4244-4349-9

Type

conf

DOI

10.1109/ISIE.2009.5213592

Filename

5213592