title: Delay-slope-dependent stability results of recurrent neural networks.
abstract: By using the fact that the neuron activation functions are sector bounded and nondecreasing, this brief presents a new method, named the delay-slope-dependent method, for stability analysis of a class of recurrent neural networks with time-varying delays. This method includes more information on the slope of neuron activation functions and fewer matrix variables in the constructed Lyapunov-Krasovskii functional. Then some improved delay-dependent stability criteria with less computational burden and conservatism are obtained. Numerical examples are given to illustrate the effectiveness and the benefits of the proposed method.
authors: Department of Information and Communication, Nanjing University of Information Science and Technology, Nanjing 210044, China. litaojia79@yahoo.com.cn
authors: Tao
authors: T
authors: Li
authors: Wei Xing
authors: WX
authors: Zheng
authors: Chong
authors: C
authors: Lin
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D008962:Models, Theoretical; D016571:Neural Networks, Computer