Title :
Delay-dependent filtering of Markovian jumping neural networks with mode-dependent time delays
Author :
He Huang ; Xiaoping Chen ; Qiang Hua
Author_Institution :
Sch. of Electron. & Inf. Eng., Soochow Univ., Suzhou, China
Abstract :
This paper is concerned with the problem of delay-dependent L2 - L∞ filter design of Markovian jumping neural networks with mode-dependent time delays. By constructing a suitable stochastic Lyapunov functional, a delay-dependent condition is established such that the filtering error system is stochastically stable and a prescribed L2 - L∞ performance is achieved. Furthermore, it is shown that the gain matrices and the optimal L2 - L∞ performance index are obtained by solving a convex optimization problem subject to some linear matrix inequalities. An example is finally provided to demonstrate the effectiveness of the developed result.
Keywords :
Lyapunov methods; Markov processes; convex programming; delays; linear matrix inequalities; neural nets; Markovian jumping neural networks; convex optimization problem; delay-dependent L2- L∞ filter design; delay-dependent condition; delay-dependent filtering; filtering error system; gain matrices; linear matrix inequalities; mode-dependent time delays; optimal L2 - L∞ performance index; stochastic Lyapunov functional; Biological neural networks; Delay effects; Delays; Linear matrix inequalities; State estimation; Symmetric matrices; Convex Optimization; Filtering; Markovian Jumping Neural Networks; Time Delays;
Conference_Titel :
Control Conference (CCC), 2013 32nd Chinese
Conference_Location :
Xi´an