Exponential Stability And Synchronization Of Fuzzy Neural Network

Neural network systems have many applications, such as in signal processing, combinatorial optimization, automatic control, pattern recognition. Varying delay and uncertainty are common elements in neural network controlling, which may influence the stability of the entire network. In this paper, the stability, synchronization, and stochastic systems adaptive synchronization of fuzzy neural network delay are discussed. By using Lyapunov stability theory and the theory of Dini derivatives, the exponential stability and synchronization are investigated.The first chapter includes an overview of some of the fuzzy neural network applications, development status in China and abroad, the paper content, the overall structure and some of the innovations.The second chapter gives the relevant prior knowledge, including relevant definitions, exponential stability, exponential synchronization, adaptive synchronization theory and the lemmas to be used.In the third chapter, exponential stability is considered for a class of fuzzy neural networks with distributed delays. Some sufficient conditions for exponential stability of the systems are derived by Lyaponov functional and mean inequality. Numerical examples are used to show the advantage of the results of this paper.In the forth chapter, exponential synchronization is considered for a class of fuzzy neural networks with distributed delays. By applying Dini derivative and introducing many real parameters, and estimating the upper bound of function, some sufficient conditions on the exponential synchronization of fuzzy neural networks are established. Designing examples and comparing the current findings show the results of this paper are feasible and superior.In the fifth chapter, the adaptive synchronization is investigated for a class of fuzzy neural networks with stochastic perturbed chaotic. By constructing suitable Lyapunov function and using stochastic analysis, some sufficient conditions are derived to ensure the adaptive synchronization of the networks. Finally, an example is provided to demonstrate the effectiveness and comparing the current findings show the results of this paper are feasible and superior.