Based On Adaptive Neural Network Signal Processing

Artificial neural network is used widely in the signal processing. This thesis will have mainly studied the questions of the artificial neural network structure optimization and the sample information choice. The optimized neural networks are applied to forecast the non-linear time series signal and to recognize the fluorescence spectrum. In the process of constructing the artificial neural network model, how to choose the study algorithm as well as to seek the best network architecture is always a key to decide the network's performance. It is very important to choice of the network topology architecture. The inappropriate structure may cause the poor generalization ability and slow the convergence rate or non-convergence. The redundant information of the sample, which affects the correct input-output mapping of the neural network, is deleted. The Recursive Least Square (RLS) is chosen to adjust the interconnection weights. Compared with other algorithms, the RLS algorithm not only is easier to train a network because it has less parameters but also has faster convergence rate because it automatically has the optimal training stride values. In order to optimize the network's topology architecture, we introduce the pruning algorithm into the network. It can prune the neurons not only in the middle layer but also in the input layer by proposed a new energy function. Pruning the neurons of the input layer means that the redundant component of the input sample is moved. Comparing other data compression or feature extraction methods, this method is more effective for improving the prediction accuracy or correct recognition rate because the neural pruning is relative to the output error. The neural network models based on the adaptive RLS learning and pruning algorithm are used to forecast nonlinear time series signals, such as stock indexes and variable bit rate video traffic, and recognize nonlinear fluorescence spectrum of ethylene. The computer simulations have done. The mean square error of the optimized network is up to about 10^-5 for forecasting nonlinear time series signals. The correct recognition rate of the proposed model is up to 100% for recognizing nonlinear fluorescence spectrum. The experimental results show that optimal network based on the pruning algorithm not only not only reduce the calculating complexity greatly, but also improve the prediction accuracy or correct recognition rate. The presented method can be used to process real-time the signals because of its faster convergence rate.