基于改进RBF神经网络的产业损害预警指数预报  

Forecast of Industry Injury Early-warning Index based on improved RBF Neural Networks

作　　者：郭恒川[1] 任波[1] 

机构地区：[1]洛阳理工学院计算机与信息工程系,洛阳471023

出　　处：《铁路计算机应用》2011年第11期4-7,共4页Railway Computer Application

基　　金：河南省产业损害预警系统研究(0513053700);洛阳理工学院青年基金项目(2010QZ21)

摘　　要：为了有效的保护产业安全,产业损害预警指数的预报成为重要的研究方向。针对这种非线性的时间序列和产业损害预警系统的应用特点,本文对RBF网络的学习算法进行了一定的改进,提高了预测结果的稳定性。根据实验仿真结果显示,该模型优于传统使用的分析方法,为各生产行业的生产计划提供决策支持。In order to protect industry security effectively,the forecast of Industry Injury Early-warning Index became an important research direction.In view of this nonlinear time series and the characteristics of Industry Injury Early-warning System,it was improved the algorithm of RBF Neural Networks,increased the stability of forecast.The simulation results of the experiment showed that the model was superior to the traditional methods,and could provide decision support for the production plant with various enterprises.

关 键 词：RBF 神经网络 产业损害预警指数 仿真 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]