Optimizing neural network forecast by immune algorithm

来源期刊：中南大学学报(英文版)2006年第5期

论文作者：杨淑霞 李翔 李宁 杨尚东

文章页码：573 - 576

Key words：neural network; forecast; immune algorithm; optimization

Abstract: Considering multi-factor influence, a forecasting model was built. The structure of BP neural network was designed, and immune algorithm was applied to optimize its network structure and weight. After training the data of power demand from the year 1980 to 2005 in China, a nonlinear network model was obtained on the relationship between power demand and the factors which had impacts on it, and thus the above proposed method was verified. Meanwhile, the results were compared to those of neural network optimized by genetic algorithm. The results show that this method is superior to neural network optimized by genetic algorithm and is one of the effective ways of time series forecast.