The Research Of Neural Network Based On Fuzzy Parameters In Prediction Of Stock Price

This paper puts forward a fuzzy neural network system aimed at stock price prediction, and an ameliorative method on its function is studied.In training of Back-Propagation neural network, parameter adaptable method which can automatically adjust learning rate and inertia factor is employed in order to avoiding systemic error immersed in a local minimum and accelerating the network's convergence; Introduced the further optimization of the network's structure, it gives the research result of selection of the hidden layers, neurons, and the strategy of re-learning, compared the sums of the deviation square of this algorithm with conventional BP algorithm, as a result, the approach accuracy and the generalization ability of the network were extremely improved.This improved BP Algorithm was here used to predict some stock prices of Shenzhen city to verify its accuracy and rationality, For the sake of roundly characterizing the trait and rule of the stock market, the model has gathered 500 groups of primary data from 3.1st, 2001 to 4.11th, 2003, labored some factors that influence the effect on predicting the trend of stock prices by synthetically applying the means of elementary factor and technique analysis, Thus we can extract the recursive technique parameter of proper-period, describe the vary trend of stock prices in form of fuzzy time series, and predict the ascending subjection degree of the stock prices in approaching homologous period and decide its accuracy. Academic analysis and experimental results show the method is feasible and effective on shortdated prediction of stock market; As long as a proper prediction model is chosen, people can get more profits than average market earning.