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.
[1] Yang H T, Huang C M. Identification of ARMAX Model for Short Term Load Forecasting: An Evolutionary Programming Approach. IEEE Trans on Power System, 1996, 11(1): 403408 [2] Papalexopoulos A D, Hesterberg T C. A Regression Based Approach to Short Term System Load Forecasting. IEEE Trans on Power Systems, 1990, 5(4): 15351547 [3] Pan Feng, Cheng Haozhong, Yang Jingfei, et al. Power System ShortTerm Load Forecasting Based on Support Vector Machines. Power System Technology, 2004, 28(21): 3942 (in Chinese) (潘 峰,程浩忠,杨镜非,等.基于支持向量机的电力系统短期负荷预测.电网技术, 2004, 28(21): 3942) [4] Rahman S. Generalized KnowledgeBased Short Term Load Forecasting Techniques. IEEE Trans on Power Systems, 1993, 8(2): 508514 [5] Albus J S. A New Approach to Manipulator Control: The Cerebellar Model Articulation Controller. Journal of Dynamic Systems, Measurement and Control, 1975, 97(3): 220227 [6] Parks P C, Militzer J. Convergence Properties of Associative Memory Storage for Learning Control System. Automation and Remote Cotnrol, 1989, 50(10): 254286 [7] Commuri S, Jagannathan S, Lewis F L. CMAC Neural Network Control of Robot Manipulators. Journal of Robotic Systems, 1997, 14(6): 465482 [8] He Jianchun, Wang Huiyan. Application of CMAC Neural Network to Nonlinear Predictive Control. Control and Decision, 2002, 17(1): 9295 (in Chinese) (何剑春,王慧艳.CMAC网络建模在非线性预测控制中的应用.控制与决策, 2002, 17(1): 9295) [9] Ruan Qing, Wang Yiqiang. PCA Approach to BP Learning. Journal of Fudan University: Natural Science, 2005, 44(2): 318322,327 (in Chinese) (阮 庆,王逸蔷.主成份分析法在BP学习中的应用.复旦学报:自然科学版, 2005, 44(2): 318322,327) [10] Qin Ting, Chen Zonghai, Zhang Haitao, et al. A Learning Algorithm of CMAC Based on RLS. Neural Processing Letters, 2004, 19(1): 4961 [11] Thompson D E, Kwon S. Neighbor Sequential and Random Training Techniques for CMAC. IEEE Trans on Neural Networks, 1995, 6(1): 196202 [12] Wang Shengfu. Spline Function and Their Application. Fremont, USA: Northwestern Polytechnical University Press,1989 [13] Chen S, Cowan C F N,Grant P M. Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks. IEEE Trans on Neural Networks, 1991, 2(2): 302309 [14] Chen Jianhua, Zhou Hao. ShortTerm Electricity Price Forecasting Using Cerebellar Model Articulation Controller Neural Network. Power System Technology, 2003, 27(8): 1620 (in Chinese) (陈建华,周 浩.基于小脑模型关节器神经网络的短期电价预测,电网技术,2003,27(8):1620)