title: Exponential stabilization for sampled-data neural-network-based control systems.
abstract: This paper investigates the problem of sampled-data stabilization for neural-network-based control systems with an optimal guaranteed cost. Using time-dependent Lyapunov functional approach, some novel conditions are proposed to guarantee the closed-loop systems exponentially stable, which fully use the available information about the actual sampling pattern. Based on the derived conditions, the design methods of the desired sampled-data three-layer fully connected feedforward neural-network-based controller are established to obtain the largest sampling interval and the smallest upper bound of the cost function. A practical example is provided to demonstrate the effectiveness and feasibility of the proposed techniques.
authors.forename: Zheng-Guang
authors.initials: ZG
authors.lastname: Wu
authors.forename: Peng
authors.initials: P
authors.lastname: Shi
authors.forename: Hongye
authors.initials: H
authors.lastname: Su
authors.forename: Jian
authors.initials: J
authors.lastname: Chu
mesh_terms: D016208:Databases, Factual / Q000706:statistics & numerical data; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics