title: Passivity of switched recurrent neural networks with time-varying delays.
abstract: This paper is concerned with the passivity analysis for switched neural networks subject to stochastic disturbances and time-varying delays. First, using the multiple Lyapunov functions method, a state-dependent switching law is designed to present a stochastic passivity condition. Second, a hysteresis switching law involving both the current state and the previous value of the switching signal are presented to avoid chattering resulted from the state-dependent switching. Third, based on the average dwell-time approach, a class of switching signals is determined to guarantee the switched neural network stochastically passive. Finally, three numerical examples are provided to illustrate the characteristics of three proposed switching laws.
authors.forename: Jie
authors.initials: J
authors.lastname: Lian
authors.forename: Jun
authors.initials: J
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D008432:Mathematical Computing; D016571:Neural Networks, Computer; D013997:Time Factors