Complex System Behavior Forecasting Method Based on BMACRLS Model and Its Application
YANG XiaoYu1,2, FU ZhongQian1, WANG WeiPing2
1.Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027 2.Department of Information Management and Decision Science, University of Science and Technology of China, Hefei 230026
Abstract：;Complex system behavior forecasting is quite important in complex system management and decision. It is the key of improving forecasting stability and extension without the loss of precision. A new method based on PCA (principle component analysis) and CMACRLS (recursive least squares) is proposed. PCA is used to reduce the input space dimensions. CMACRLS algorithm combined with the Bspline is introduced to ensure the weight convergence and provide the differential information of function adapted to the online modeling. Then the load forecasting is performed by PCABMACRLS and RBF neural network on the data of FuYang Power Land in 2004. The result comparison between two algorithm illustrates the validity of the proposed method.
杨晓宇，傅忠谦，王卫平. 基于BMACRLS模型的复杂系统行为预测方法及其应用*[J]. 模式识别与人工智能, 2007, 20(2): 266-270.
YANG XiaoYu , FU ZhongQian , WANG WeiPing. Complex System Behavior Forecasting Method Based on BMACRLS Model and Its Application. , 2007, 20(2): 266-270.
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