RBF神经网络最优分割算法及其在股市预测中的应用

An Optimal Partition Algorithm of RBF Neural Network and Its Applications to Stock Price Prediction

将最优分割算法(optimal partition algorithm,OPA)用于径向基函数神经网络参数的训练中.对OPA进行了适当的改进,在改进的OPA中增加了类的中心与宽度的确定方法,并将它们用于确定RBF网络的中心与宽度,提出了利用类的目标函数的差分对网络结构进行动态调整的方法,从而实现了隐节点数的自适应选择。用于股价预测的数值模拟结果验证了该方法的有效性。与传统算法进行比较的结果表明,在预测方面OPA具有较明显的优势,将OPA算法与正交最小二乘法相结合的OPA-OLS算法可以提高趋势预测的正确率。

The optimal partition algorithm (OPA) is applied to the training of parameters in the radial basis function (RBF) neural network. The appropriate modification for the OPA is performed according to the characteristics of the RBF neural network. The approach for determining the centers and widths of the clustering is added in the modified OPA and applied to choose the centers and widths of the neural network. A method for adjusting the structure of the neural network dynamically is presented by using the difference of the objective functions of the clustering. Thus it is realized to select the number of the hidden nodes adaptively. Simulation results of the stock price prediction demonstrate the effectiveness of the proposed approach. Comparisons with traditional algorithms show that the proposed OPA method possesses obvious advantages in the precision of forecasting, generalization, and forecasting trends. Simulations also show that the algorithm combining the OPA with the orthogonal least squares (OLS) possesses more superior performance in the rightness of forecasting trends.