title: Mean square delay dependent-probability-distribution stability analysis of neutral type stochastic neural networks.
abstract: The aim of this manuscript is to investigate the mean square delay dependent-probability-distribution stability analysis of neutral type stochastic neural networks with time-delays. The time-delays are assumed to be interval time-varying and randomly occurring. Based on the new Lyapunov-Krasovskii functional and stochastic analysis approach, a novel sufficient condition is obtained in the form of linear matrix inequality such that the delayed stochastic neural networks are globally robustly asymptotically stable in the mean-square sense for all admissible uncertainties. Finally, the derived theoretical results are validated through numerical examples in which maximum allowable upper bounds are calculated for different lower bounds of time-delay.
authors: School of Mathematics, Madurai Kamaraj University, Madurai 625 021, Tamil Nadu, India. Electronic address: muralimku@gmail.com.
authors: S
authors: S
authors: Muralisankar
authors: School of Mathematics, Madurai Kamaraj University, Madurai 625 021, Tamil Nadu, India. Electronic address: manivannangru@gmail.com.
authors: A
authors: A
authors: Manivannan
authors: Department of Mathematics, Gandhigram Rural Institute - Deemed University, Dindigul 624 302, Tamil Nadu, India. Electronic address: balugru@gmail.com.
authors: P
authors: P
authors: Balasubramaniam
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D016014:Linear Models; D016571:Neural Networks, Computer; D013269:Stochastic Processes; D035501:Uncertainty
keywords: Linear matrix inequality; Lyapunov–Krasovskii functional; Neutral type stochastic neural networks; Random delay