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Robust fuzzy control of Takagi-Sugeno fuzzy neural networks with discontinuous activation functions and time delays

Robust fuzzy control of Takagi-Sugeno fuzzy neural networks with discontinuous activation functions and time delays
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摘要 The problem of global robust asymptotical stability for a class of Takagi-Sugeno fuzzy neural networks（TSFNN） with discontinuous activation functions and time delays is investigated by using Lyapunov stability theory.Based on linear matrix inequalities（LMIs）,we originally propose robust fuzzy control to guarantee the global robust asymptotical stability of TSFNNs.Compared with the existing literature,this paper removes the assumptions on the neuron activations such as Lipschitz conditions,bounded,monotonic increasing property or the right-limit value is bigger than the left one at the discontinuous point.Thus,the results are more general and wider.Finally,two numerical examples are given to show the effectiveness of the proposed stability results. The problem of global robust asymptotical stability for a class of Takagi-Sugeno fuzzy neural networks（TSFNN） with discontinuous activation functions and time delays is investigated by using Lyapunov stability theory.Based on linear matrix inequalities（LMIs）,we originally propose robust fuzzy control to guarantee the global robust asymptotical stability of TSFNNs.Compared with the existing literature,this paper removes the assumptions on the neuron activations such as Lipschitz conditions,bounded,monotonic increasing property or the right-limit value is bigger than the left one at the discontinuous point.Thus,the results are more general and wider.Finally,two numerical examples are given to show the effectiveness of the proposed stability results.
作者 Yaonan Wang Xiru Wu Yi Zuo
机构地区 College of Electrical and Information Engineering School of Energy and Power Engineering
出处 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2011年第3期473-481,共9页 系统工程与电子技术（英文版）
基金 supported by the National Natural Science Foundation of China(60775047 60835004) the National High Technology Research and Development Program of China(863 Program)(2007AA04Z244 2008AA04Z214) the Graduate Innovation Fundation of Hunan Province(CX2010B132)
关键词 delayed neural network global robust asymptotical stability discontinuous neuron activation linear matrix inequality（LMI） Takagi-sugeno（T-S） fuzzy model. delayed neural network global robust asymptotical stability discontinuous neuron activation linear matrix inequality（LMI） Takagi-sugeno（T-S） fuzzy model.
分类号 TP18 [自动化与计算机技术—控制理论与控制工程] TP316.86 [自动化与计算机技术—计算机软件与理论]
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Journal of Systems Engineering and Electronics
2011年 第3期

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