Passivity of Switched Recurrent Neural Networks With Time-Varying Delays

• Published in: IEEE transaction on neural networks and learning systems Vol. 26; no. 2; pp. 357 - 366
• Main Authors: Lian, Jie, Wang, Jun
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial Intelligence; Average dwell time; Biological neural networks; Computer Simulation; Delay; Delays; Hysteresis; hysteresis switching law; Law; Lyapunov functions; Lyapunov methods; Mathematical Computing; Neural networks; Neural Networks (Computer); Passivity; Recurrent neural networks; Stochasticity; switched neural networks; Switches; Switching; Symmetric matrices; Time Factors; Vibration; hysteresis switching law; passivity; switched neural networks; Average dwell time
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2014.2379920

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.