基于改进粒子群优化算法的灰色神经网络模型

Grey Neural Network Model Based on Modified Particle Swarm Optimization Algorithm and Its Application

根据灰色神经网络的参数随机选择类似于粒子群算法中的粒子初始空间位置,采用改进粒子群算法代替梯度修正法,对网络参数进行了处理,并通过寻找粒子群算法中的最优个体,建立了基于改进粒子群算法的灰色神经网络,提高了预测模型的稳健性和精度.通过解决短期订货量问题,与反向传播(BP)神经网络、灰色神经网络、没有改进的粒子群灰色神经网络算法和基于遗传算法的灰色神经网络等方法进行了比较.分析结果表明,基于改进粒子群算法的灰色神经网络计算更为方便,并具有更好的逼近能力和预测精度.为优化网络模型参数提供了一种新方法,并拓展了预测模型的研究思路.

A grey neural network model is established with a modified particle swarm optimization （PSO） instead of the gradient correction method. The initial positions of the particles are chosen randomly according to the parameters of grey neural networks which are processed through PSO and the best individual in particle swarm algorithm is searched to improve robustness and precision of the forecasting model. Through testing the effect of solving short-term order problem, the model proves to be simple with better forecast precision and of a higher approximation capability compared with back propagation （BP） neural network, grey neural network, the traditional particles warm optimizer and BP neural network. The paper presents a new method for optimizing network parameters and some new ideas for researches on forecasting model.