Study On New Fuzzy Chaotic Neural Networks Model And Its Character

The recognition for human brain's local function has improved in the recent years. But the cognition of human brain's working process as a whole is still obscure. Both of fuzzy logic and chaos dynamic are internal features of human brain. Therefore, to fuse artificial neural networks, fuzzy logic and chaotic dynamic together to constitute fuzzy chaotic neural networks is a novelty method. It is helpful to simulate the whole function of human brain, and very meaningful in disposing nonlinear and associate memory problems from the view of both theory and reality. Study on fuzzy chaotic neural networks technology is still being a preliminary exploration phase. This dissertation is focus on the new ways of fuzzy neural networks construction and its application based on the existing achievement of this field.Firstly, a new chaotic Back-Propagation hybrid learning algorithm is proposed to training fuzzy radial basis function neural network. The analysis of chaotic characters and probability density is processed aiming at a few of typical chaotic mapping functions, which offers theoretical foundation for the choosing of chaotic mapping function in the construction process of chaotic Back-Propagation algorithm. A two stage learning method of fuzzy radial basis function neural network is given subsequently. The proposed chaotic Back-Propagation hybrid learning algorithm could be adaptively adjusted according to the parameters, and the convergence is guaranteed by annealing coefficient. The effectiveness of algorithm is validated throughout approximate chaos time series.Then, a chaotic recurrent fuzzy neural networks model is developed based on existing recurrent fuzzy neural networks. The mathematical model of neural networks and weight learning formulas based on dynamic Back-Propagation algorithm are deduced. After that, the convergence of learning algorithm is analyzed. A theorem is deduced to verify the range of adaptive learning rate. Using adaptive networks based fuzzy inference systems, recurrent fuzzy neural networks, chaotic recurrent fuzzy neural networks and chaotic recurrent fuzzy neural networks with adaptive adjustment learning rate to construct and simulate two typical nonlinear chaotic systems respectively. The validity of the proposed model and algorithm is testified at last.Next, the chaotic feature of dynamic fuzzy neuron with differential links is studied. The dynamic fuzzy neural networks are analyzed in the same way subsequently. Throughout the analysis, the necessary condition for single dynamic fuzzy neuron to have a dissipation and the necessary condition for its Lyapunov exponents to be more than zero are deduced. The sufficient condition of dynamic fuzzy neural networks to be a dissipation system and the necessary condition for its Lyapunov exponents to be more than zero are also list.At last, according to the composition principle of fuzzy Hopfield neural network and the trait of self-evolution neural network, a self-evolution fuzzy chaotic neural network is proposed. The chaotic character and period character of self-evolution neural networks are analyzed at first. And then the model is going through a fuzzification process to constitute a self-evolution fuzzy chaotic neural network. The convergence and stability of the model during the process of fuzzy clustering are proved, and the associate memory ability is analyzed. As shown by the result of the simulation test, the self-evolution fuzzy chaotic neural networks could not only accomplish the fuzzy cluster function, but also realized chaotic associate memory.