The invention relates to the technical field of optimization of the
wavelet neural network, in particular to a
wavelet neural network weight initialization method based on Bayes
estimation, and the state
estimation and search idea is adopted in the
wavelet neural network weight initialization method. The
wavelet neural network weight initialization method based on Bayes
estimation comprises the steps of building a
wavelet neural network model, unitizing weights, inputting and optimizing wavelet nerve
cell weights, and optimizing weights of
nerve cells of an output layer.
Wavelet neural network weight parameters are linked with the
network structure, wavelet types, input data and output target values, the state estimation idea and theory are introduced into initial setting of the weight parameters, wavelet network learning and training capacity is enhanced, the wavelet network has certain pertinence in the initialization phase, and therefore the adaptability of the weights in follow-up network learning and training is improved. Compared with a traditional weight initialization method, the learning efficiency can be effectively improved,
oscillation amplitude of
network output can be reduced, the rate of
algorithm convergence is improved, and
network output divergence caused by improper weights can be avoided.