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《Journal of Computer Research and Development》 2002-09
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ANALYSIS OF GLOBAL EXPONENTIAL STABILITY OF SECOND ORDER NEURAL NETWORKS

XU Bing-Ji ①, LIAO Xiao-Xin ②, and LIU Xin-Zhi ①② ①(Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074) ②(Department of Applied Mathematics, University of Waterloo, Waterloo Can  
When the neural network applies to optimal calculation, the ideal situation is that there is a unique equilibrium point which is globally asymptotically stable and the neural network tends to the equilibrium point at the speed of exponent. This can reduce the calculation time. The second order neural networks have faster convergence rate than the ordinary neural networks. Global exponential stability of equilibrium point for a class of second order Hopfield type neural networks is discussed by the Lyapunov method. Some criteria for global exponential stability of equilibrium point are obtained. As a special case, several new global exponential stability criteria are obtained for the corresponding Hopfield neural networks.
【Fund】: 国家自然科学基金 ( 6 0 0 740 0 8);; 高校博士点基金 ( 2 0 0 10 4870 0 5 )资助
【CateGory Index】: TP183
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1 GUAN Zhi hong,SUN De bao,SHEN Jian jing(Department of Control Science and Engineering,Huazhong University of Science and Technology,Wuhan 430074,China);Qualitative Analysis of High-Order Hopfield Neural Networks[J];ACTA ELECTRONICA SINICA;2000-03
【Co-citations】
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1 ZHANG An-cai, FENG Guo-tao (School of Mathematical Science and Computing Technology, Central South University, Changsha Hunan 410083);Existence and Global Exponential Stability of Periodic Solutions for High-order Cellular Neural Networks with Time-varying Delays[J];Journal of Hunan University of Arts and Science(Science and Technology);2007-02
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9 LU Li 1,ZHANG Ji-ye 2,YANG Yi-ren 1( 1.Department of Applied Mechanics and Engineering,Southwest Jiaotong University,Chengdu,Sichuan 610031,China; 2.Traction Power National Laboratory,Southwest Jiaotong Unversity,Chengdu,Sichuan 610031,China);Global Exponential Stability in Delayed Hopfield Neural Network Models[J];Acta Electronica Sinica;2002-10
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1 LIAO Xiao xin 1,XIAO Dong mei 2(1 Department of Control Science and Engineering,Huazhong University of Science and Technology,Wuhan 430074,China; 2 Department of Mathematics,Huazhong Normal University,Wuhan 430074,China);Globally Exponential Stability of Hopfield Neural Networks with Time varying Delays[J];ACTA ELECTRONICA SINICA;2000-04
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