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证券市场是一个高风险高收益的投资市场,获取比较高的收益同时降低风险是投资者追求的目标,径向基函数(RadialBasisFunction,RBF)神经网络以其简单的结构,优良的全局逼近性能而引起了学者们的广泛关注。由于RBF神经网络的种种优越性,使得它在函数逼近和非线性时间序列预测等方面得到广泛应用。将RBF神经网络应用在股市趋势预测中,以上证指数作为对象进行建模与预测,结果表明,此种网络具有较好的学习和泛化能力,在股市趋势预测中取得了较好的效果。
The securities market is a high-risk and high-yielding investment market. It is the goal pursued by investors to obtain higher returns while reducing risk. Radial Basis Function (RBF) neural network, with its simple structure and excellent global approximation performance, Aroused the widespread concern of scholars. Due to the advantages of RBF neural network, it is widely used in function approximation and nonlinear time series prediction. The application of RBF neural network in the stock market trend prediction, the Shanghai Composite Index as the object of modeling and prediction, the results show that this network has good learning and generalization ability, the stock market trend forecasting has achieved good results.