一种RBF神经网络的混合学习算法在CPI中的应用

摘要

根据RBF神经网络最常用的OLS算法、K-均值聚类算法和梯度下降训练学习算法,提出了一种基于正交最小二乘K-均值聚类梯度下降优化的RBF神经网络的混合算法.该算法克服了单一某种训练方法的不足,发挥了混合算法的长处,进行了CPI预测的仿真实验.结果证明:该方法是有效实用.%This paper proposes an optimized Hybrid algorithm based on K-means clustering, orthogonal least squares (OLS) and Gradient descent algorithm. By applying K-means clustering and OLS algorithm to train the central position and width of the basis function adopted in the RBFNN, and computing the network's weights with least-squaremethod,In addition,by combining the gradient algorithm,via minimizing the objective function to adjust the data center and width of the hidden nodes and weights of output, the optimization of RBF neural network is achieved. For testing purposes,The optimized Hybrid network is applied to the CPI Forecasting, Experimental results reveal that the predictions using the proposed approach are consistently better than those obtained using the single prediction methods presented in this study in terms of the same measurements.