Dissertation
Dissertation > Industrial Technology > Automation technology,computer technology > Automated basic theory > Artificial intelligence theory > Artificial Neural Networks and Computing

Stability Analysis of Three Types of Uncertain Neural Networks with Time Delays

Author XuZengHui
Tutor ChenYiMing
School Yanshan University
Course Operational Research and Cybernetics
Keywords Neural Network Stability Analysis Linear matrix inequality Delay Pulse
CLC TP183
Type Master's thesis
Year 2010
Downloads 19
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The neural network is a highly integrated interdisciplinary , it is able to simulate human intelligent behavior . In recent years, the neural network has become one of the \Stability of the system to normal operation of the premise , and robustness is the key to survival in the case of abnormal and dangerous system , so the study of neural network system stability and robustness of great significance . Papers using the Lyapunov stability theory and linear matrix inequality technique to study the stability of the three types of uncertain time-delay neural network problems , ensure the stability of the system . Reduced as compared with the existing results conservative . First, Uncertain Stochastic Neural Networks Robust Stability in Mean Square . A class given in the form of linear matrix inequalities , delay-dependent global robust mean square exponential stability criterion is obtained by constructing a new Lyapunov function . The numerical example illustrates the effectiveness and superiority of the criteria given . Secondly , we study a class of a Markov chain with time-varying delay pulse random Cohen - Grossberg neural network model , the application of some inequality techniques and stability theory , model robust mean square gradually theorem nearly stability . The stability theorem has better applicability . Finally, simulation examples demonstrate the validity of the theorem . Again, for a class of discrete random neural network model with discrete and distributed delays , by constructing a new Lyapunov function gives the the model asymptotic stability theorem . Theorem, given in the form of linear matrix inequalities easily solved using Matlab LMI toolbox . Finally, simulation examples demonstrate the validity of the theorem .

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