Enhanced Results on Sampled-Data Synchronization for Chaotic Neural Networks With Actuator Saturation Using Parameterized Control
Seong-Hyeon Jo, Wookyong Kwon, Sang Jun Lee, Sangmoon Lee, Yongsik Jin
Abstract
This article investigates a novel sampled-data synchronization controller design method for chaotic neural networks (CNNs) with actuator saturation. The proposed method is based on a parameterization approach which reformulates the activation function as the weighted sum of matrices with the weighting functions. Also, controller gain matrices are combined by affinely transformed weighting functions. The enhanced stabilization criterion is formulated in terms of linear matrix inequalities (LMIs) based on the Lyapunov stability theory and weighting function's information. As shown in the comparison results of the bench marking example, the presented method much outperforms previous methods, and thus the enhancement of the proposed parameterized control is verified.