基于混合算法优化神经网络的混沌时间序列预测

提出了一种混合算法优化神经网络的混沌时间序列预测模型.将粒子群优化算法与模拟退火算法过程中概率突跳的思想相结合形成一种新的混合算法,并用此混合算法优化神经网络建立预测模型.该模型克服了传统的神经网络收敛慢、易陷入局部最优等不足.利用该模型对Mackey-Glass混沌时间序列和Henon映射进行实验仿真,结果表明,该模型收敛速度快,稳定性能好,预测精度高.

Prediction of Chaotic Time Series Based on Neural Network Optimized by Hybrid Algorithm

A prediction model for time series was introduced, which uses the hybrid algorithm to optimize the neural networks. The main idea was to build the new hybrid algorithm by combining particle swarm optimization with Simulated Annealing in sudden jump, and then to optimize the neural networks with this hybrid algorithm. Therefore, many shortcomings, like the slow convergence of common neural networks, partial optimization and the prediction precocity of simplex particle swarm optimization, were overcome. In addition, in order to prove the validity and the value of the model, the Mackey-Glass chaotic time series and the Henon map were simulated. The results have shown the fast convergence, good stability and the high precision of this model.