title: Stability and L2 performance analysis of stochastic delayed neural networks.
abstract: This brief focuses on the robust mean-square exponential stability and   L(2) performance analysis for a class of uncertain time-delay neural networks perturbed by both additive and multiplicative stochastic noises. New mean-square exponential stability and   L(2) performance criteria are developed based on the delay partition Lyapunov-Krasovskii functional method and generalized Finsler lemma which is applicable to stochastic systems. The analytical results are established without involving any model transformation, estimation for cross terms, additional free-weighting matrices, or tuning parameters. Numerical examples are presented to verify that the proposed approach is both less conservative and less computationally complex than the existing ones.
authors.affiliation: School of Computing and Mathematics, University of Western Sydney, Penrith NSW 2751, Australia. cloudscy@hdu.edu.cn
authors.forename: Yun
authors.initials: Y
authors.lastname: Chen
authors.forename: Wei Xing
authors.initials: WX
authors.lastname: Zheng
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012984:Software; D012985:Software Design; D013269:Stochastic Processes