中文摘要：

复杂系统行为预测是复杂系统管理与决策的重要内容．为了在确保预测准确性的前提下提高系统预测的稳定性和泛化能力，提出一种基于主成分分析结合加入B样条的连续CMAC递推最小二乘算法（CMAC—RLS）的组合模型的预测方法（PCA-BMAC-RLS）．首先利用主成分分析来降低输入变量的维数以减少CMAC权系数空间．其次采用BMAC—RLS算法以确保权值的收敛且能提供函数的微分信息以适合复杂系统的在线建模．最后以实际应用为例，对比采用RBF神经网络模型和本文的PCA-BMAC-RLS组合模型的预测实验．实验结果显示，本文方法具有稳定性好、泛化能力强、运行速度快、预测精度高等显著优点．

英文摘要：

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 CMAC-RLS （recursive least squares） is proposed. PCA is used to reduce the input space dimensions. CMAC-RLS algorithm combined with the B-spline 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 PCA-BMAC-RLS 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.