Title :
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
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;
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
DOI :
10.1109/ISIE.2009.5213592
