Enhanced Global Asymptotic Stabilization Criteria for Delayed Fractional Complex-valued Neural Networks with Parameter Uncertainty
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This paper addresses the global asymptotic stabilization of delayed fractional complex-valued neural networks (FCVNNs) subject to bounded parameter uncertainty. The problem is proposed for two reasons: 1) The available methods for uncertain dynamical systems may be too conservative; 2) The existing algebraic conditions will lead to huge computational burden for large-scale FCVNNs. To surmount these difficulties, the delayed FCVNNs with interval parameters are transformed into a tractable form at first. Then, a simple and practical controller–linear state feedback controller is designed to achieve the global asymptotic stabilization. By constructing different Lyapunov functions and utilizing the fractional-order comparison principle and interval matrix method, two sufficient global asymptotic stabilization criteria expressed in LMI forms, are established. The obtained results in this paper improve and extend some previous published results on FCVNNs. Finally, two numerical examples are provided to illustrate the correctness of the theoretical results.
KeywordsComplex-valued neural networks fractional-order systems global asymptotic stabilizatio interval matrix methodn LMI condition
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