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Finite-Time Stability for Fractional-Order Bidirectional Associative Memory Neural Networks with Time Delays 被引量:1

Finite-Time Stability for Fractional-Order Bidirectional Associative Memory Neural Networks with Time Delays
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摘要 This paper is concerned with fractional-order bidirectional associative memory(BAM) neural networks with time delays. Applying Laplace transform, the generalized Gronwall inequality and estimates of Mittag–Leffler functions, some sufficient conditions which ensure the finite-time stability of fractional-order bidirectional associative memory neural networks with time delays are obtained. Two examples with their simulations are given to illustrate the theoretical findings. Our results are new and complement previously known results.
作者 Chang-Jin Xu 徐昌进;李培峦;庞一成(Guizhou Key Laboratory of Economics System Simulation, Guizhou University of Finance and Economics, Guiyang 550004, China School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, China School of Mathematics and Statistics, Guizhou University of Finance and Economics, Guiyang 550004, China)
出处 《Communications in Theoretical Physics》 SCIE CAS CSCD 2017年第2期137-142,共6页 理论物理通讯(英文版)
基金 Supported by National Natural Science Foundation of China under Grant Nos.61673008,11261010,11101126 Project of High–Level Innovative Talents of Guizhou Province([2016]5651) Natural Science and Technology Foundation of Guizhou Province(J[2015]2025 and J[2015]2026) 125 Special Major Science and Technology of Department of Education of Guizhou Province([2012]011) Natural Science Foundation of the Education Department of Guizhou Province(KY[2015]482)
关键词 BAM neural networks finite-time stability time delay Gronwall inequality 双向联想记忆神经网络 时间稳定性 分数阶 Gronwall 时滞神经网络 不等式估计 拉氏变换 充分条件
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