Finite-Time Stabilization for Fuzzy Complex-Valued Neural Networks With Mixed Delays via Comparison Approach
Yunge Liu, Ziye Zhang, Xianghua Wang, Zhen Wang, Chong Lin
Abstract
This article explores deeply the finite-time stabilization for the fuzzy complex-valued neural networks (CVNNs) model with discrete delays and distributed delays. Based on the quadratic norm and one norm in complex domain, we construct the appropriate comparison functions and design the controllers without the delay information. Then, we establish algebraic criteria to guarantee finite-time stabilization for fuzzy CVNNs with multiple time delays by exploiting the comparison approach and inequality techniques. Different from applying the finite-time stability theorem to deal with finite-time control problems of delayed systems, we combine the comparison strategy with the nonseparation method, which provides a cornerstone to analyze the finite-time control of complex-valued systems with time delays. Finally, numerical simulations are conducted to testify the availability of theoretical researches.