According to the difficulty, a nonlinear Resilient back PROPagation (RPROP) neural network was proposed to estimate the chaotic sequences.

Based on the BP neural networks,this paper presents a new approach for the analog circuit fault diagnosis,which can overcome a set of difficulties,such as the existing prob- lems in storage,timeconsuming in computing a single hard fault dectionary as well as the treatment of tolerance and noize.

A ANN(artificial neural network)--an elastic neural network is constructed by applying the identifying principle of neural network patterns, overcoming better the show convergence and local minimium occurring in BP neural network grads algorithm.

The paper constructed a BP neural network on the basis of supervised learning,and built a model of extraction of gas disaster information by training the BP neural network.

To circumvent the transmission performance degradation of the orthogonal frequency division multiplexing(OFDM) systems due to the nonlinear high power amplifiers(HPA),a new predistorter is presented which consists of two similar single-input and single-output BP neural networks(NN) in series.

The composite close-loop intelligent PID control algorithm based on back-propagation neural network is presented in this paper.

Back-propagation neural network(BP) and Support Vector Machine(SVM) models can improve identification rates of 31 P MRS to 92.

This paper analyzed the basic theory and algorithm of the probabilistic neural network,and established certain equipment fault classification model based on the PNN and improved BPNN,simulation showed that PNN model outperforms the improved back-propagation neural network model in classification speed,precision and generalization ability.