Robust Exponential Stability of Switched Complex-Valued Neural Networks with Interval Parameter Uncertainties and Impulses
Xiaohui Xu,
Huanbin Xue,
Yiqiang Peng,
Quan Xu,
Jibin Yang
Affiliations
Xiaohui Xu
Key Laboratory of Fluid and Power Machinery, Ministry of Education and Key Laboratory of Automobile Measurement and Control & Safty, School of Automobile & Transportation, Xihua University, Chengdu 610039, China
Huanbin Xue
College of Mathematics and Statistics, Hanshan Normal University, Chaozhou 521041, China
Yiqiang Peng
Key Laboratory of Fluid and Power Machinery, Ministry of Education and Key Laboratory of Automobile Measurement and Control & Safty, School of Automobile & Transportation, Xihua University, Chengdu 610039, China
Quan Xu
School of Mechanical Engineering, Xihua University, Chengdu 610039, China
Jibin Yang
Key Laboratory of Fluid and Power Machinery, Ministry of Education and Key Laboratory of Automobile Measurement and Control & Safty, School of Automobile & Transportation, Xihua University, Chengdu 610039, China
In this paper, dynamic behavior analysis has been discussed for a class of switched complex-valued neural networks with interval parameter uncertainties and impulse disturbance. Sufficient conditions for guaranteeing the existence, uniqueness, and global robust exponential stability of the equilibrium point have been obtained by using the homomorphism mapping theorem, the scalar Lyapunov function method, the average dwell time method, and M-matrix theory. Since there is no result concerning the stability problem of switched neural networks defined in complex number domain, the stability results we describe in this paper generalize the existing ones. The effectiveness of the proposed results is illustrated by a numerical example.
