Enhanced Results on Sampled-Data Synchronization for Chaotic Neural Networks With Actuator Saturation Using Parameterized Control

• Published in: IEEE transaction on neural networks and learning systems Vol. 35; no. 8; pp. 11009 - 11023
• Main Authors: Jo, Seonghyeon, Kwon, Wookyong, Lee, Sang Jun, Lee, Sangmoon, Jin, Yongsik
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2024
• Subjects: Actuator saturation; Actuators; Behavioral sciences; Biological neural networks; chaotic neural networks (CNNs); Control systems; Government; Linear matrix inequalities; linear matrix inequality (LMI); nonlinearity; sampled-data synchronization control; Synchronization
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2023.3246426

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.