基于遗传神经网络的网格资源预测模型

研究网格资源预测问题,网格资源具有非线性、混沌变化特点,传统BP神经网络具有局部极小、收敛速度慢等缺陷,预测精度较低。为提高了网格资源预测精度,提出一种基于遗传神经网络的网格资源预测模型。利用遗传算法对BP神经网络的权值和阈值进行优化,然后采用BP神经网络对网格资源建立预测模型,最后采用网格资源时间序列进行有效性仿真。仿真结果表明,遗传神经网络有效地解决了传统BP神经网络的不足,提高了网格资源的预测精度,降低了预测误差,十分适合于非线性、混沌的网格资源时间序列预测。

Prediction Model for Grid Resources Optimized BP Neural Network Based on Genetic Algorithm

Study grid resource prediction problem.Grid source is of the characteristics of nonlinear and chaos,and the traditional BP neural network with local minima,slow convergence speed and other defects has low forecasting precision.In order to improve the prediction accuracy of grid resources,this paper proposed a grid resource prediction model based on the genetic neural network.The weights and thresholds of BP neural network were optimized by genetic algorithm,Then the BP neural network was used to establish the prediction model of grid source.Finally,the simulation experiments were carried out with grid source time series.the simulation results show that the proposed method effectively improves the deficiency of traditional BP neural network and the prediction accuracy of grid resource,and reduces the prediction error.It is very suitable for grid resources prediction with nonlinear,chaos characteristic.