摘要
提出一种用广义动态模糊神经网络预测股票价格的方法,网络结构可随模糊规则在学习过程中逐渐增长而自动调节,以达到预测最优化。通过选用实用的技术参数指标作为网络的输入变量对上证指数的收盘价进行预测,取得了较为理想的效果。
In this paper, a means using Generalized Dynamic Fuzzy Neural Network to predict the share price is presented. The network structure can be automatically adjusted along with the fuzzy rules gradual growth in learning process, so as to achieve the optimised forecas- ting. Practical technical parameters are selected as the network input variables to predict the closing price of Shanghai Composite Index and ideal results were achieved.
出处
《计算机应用与软件》
CSCD
2010年第8期238-240,297,共4页
Computer Applications and Software
关键词
广义动态模糊神经网络
上证指数
预测
技术指标
Generalized dynamic fuzzy neural network Shanghai composite index Forecasting Technical indicators
