混沌粒子群神经网络在上证指数预测中的应用

上证指数预测是一个非常复杂的非线性问题,为了提高对上证指数预测的准确性,本文采用基于混沌粒子群(CPSO)算法对BP神经网络算法改进的方法来进行预测.BP神经网络算法目前已经应用到预测、聚类、分类等许多领域,取得了不少的成果.但自身也有明显的缺点,比如易陷入局部极小值、收敛速度慢等.用混沌粒子群算法改进BP神经网络算法的基本思想是用混沌粒子群算法优化BP神经网络算法的权值和阈值,在粒子群算法中加入混沌元素,提高粒子群算法的全局搜索能力.对上证指数预测的结果表明改进后的预测方法,具有更好的准确性.

A Chaotic Particle Swarm Neural Network Algorithm and Its Application in Prediction of the Shanghai Composite Index

The Forecast of the Shanghai Composite Index is a very complex nonlinear problem. In order to increase the accuracy of Forecast, an improved BP neural network algorithm is given, based on the chaos and particle swarm optimization algorithm. Till now, the BP neural network algorithm has been successfully applied to the fields of fore- casting, clustering, classification, etc. But it also has some defects, such as easy to fall into a local minimum, having slow convergent speed. Our idea for improving the BP neural network algorithm is to optimize its weights and thresholds by using the chaos and particle swarm optimization algorithm, that is, add chaos into the particle swarm optimization algorithm to improve its global search ability. Application of our treatments to the prediction of the Shang- hai Composite Index shows that it is more accuracy than the original BP neural network algorithm.