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中立型时变时滞BAM神经网络状态估计器的设计

Design of state estimator for BAM neural networks with time-varying delays of neutral type
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摘要 基于Lyapunov-Krasovskii稳定性理论和积分等式的方法,给出闭环误差系统全局渐近稳定的时滞依赖充分条件,进一步得到状态估计器增益矩阵的表示方法,从而完成对中立型时变时滞BAM神经网络状态估计器的设计,通过仿真算例验证了所得结论的正确性和有效性。 Based on Lyapunov-Krasovskii theory and integral equality approach, the delay-dependent sufficient condition is obtained to ensure the closed-loop error system is globally asymptotically stable. Furthermore, the gain matrices of the estimator can be determined and the state estimator is designed completely. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed method.
作者 刘佳 张运喜
出处 《天津职业技术师范大学学报》 2016年第1期58-62,共5页 Journal of Tianjin University of Technology and Education
基金 国家自然科学基金资助项目(61304153) 天津市应用基础与前沿技术研究计划青年项目(15JCQNJC04200) 天津市高等学校科技发展基金资助项目(20120828) 天津职业技术师范大学科研启动基金资助项目(KYQD12013)
关键词 双向联想记忆(BAM)神经网络 状态估计器 LYAPUNOV-KRASOVSKII泛函 时变时滞 中立型 bidirectional associative memory(BAM) neural networks state estimator Lyapunov-Krasovskii functional time-varying delays neutral type
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参考文献8

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