混沌RBF神经网络在电力负荷预测中的应用

混沌和RBF神经网络相结合的方法，可以充分利用混沌的随机性、初值敏感性等特点，也可以充分利用RBF神经网络的大规模并行处理、自组织自适应性等功能，因此，受到了许多研究者的青睐。本文研究了混沌RBF神经网络，利用RBF神经网络的学习、逼近能力，结合混沌时间序列的嵌入维数、时延等参数构造了混沌RBF神经网络，分别对典型混沌序列及混沌RBF神经网络的建模预测进行仿真，并将RBF神经网络应用于油田电力负荷预测中。仿真分析和实用结果表明，混沌RBF神经网络具有预测时间短、预测精度高等优点，具有较高的指导意义和应用价值。

Application of the chaotic RBF neural network on Electrical Loads Prediction

The method of combining chaos and RBF neural network can take full advantages of the randomness, initial value sensitivity and so on of chaos, it can also make full use of the large-scale parallel processing, self-organization and adaptive capability of RBF neural networks. Therefore, the RBF neural network with the characteristics of chaos becomes the favorite of many researchers. In this paper, Chaotic RBF neural networks analysis theory and method are researched, these chaotic RBF neural networks are achieved by using the learning, approaching capacity of RBF neural network and the parameters such as the embedded dimension and the delay of chaotic time series, the typical chaotic sequence and modeling forecast of chaotic RBF neural network are simulated. Furthermore, RBF network is applied to electrical loads prediction. The results show that the proposed model has advantages of short prediction time, High-precision for forecasting ,etc, having a high significance and value.