A Model For Tred Of Stock Based On Rough Sets And Network Of Radical Basis Function

So far, people have been looking for all kinds of effective methods to avoid the risk of stock market and to obtain higher returns from stocks, so many technologies for pretending the trend of stock market have been produced. For the complexity of the stock market, many technologies have exposed their shortcomings and insufficiency.In this paper, the author bases on the previous studies and uses rough sets and neural network to predict the trend of stock price. The main contends are as follows:(1)At first, obtain the original data from stock market using Da Zhihui Software, and then use rough sets to extract representative data from original data. In this process, a new algorithm based on quantum computing and genetic algorithm has been used and has some advantages compared with other algorithms.(2)Considering the various advantages of RBF network, the author chooses it for establishing the pretending model.(3)As there are many learning methods for RBF network, so choosing the effective method has been the focus. In this paper, the author compares four algorithms and chooses the best from them through experiments.(4)At last, the author establishes a model based on the front research. The input vectors are choosed from the rough sets results; the output vector is divided six kind and drawned in the result chart to help users make decisions more effectively.Finally, the author uses representative data to validate the correctness and credibility of the model .