数据采集与处理2016，Vol.31Issue(5)：927-940,14.DOI:10.16337/j.1004-9037.2016.05.009
最优实验设计与Laplacian正则化的WNN的非线性预测控制
Nonlinear Predictive Control of WNN Using Optimal Experimental Design and Lapla-cian Regularization
摘要
Abstract
A nonlinear predictive control algorithm based on wavelet neural network (WNN)integrating optimal experimental design with manifold regularization is presented for the complex processes.Firstly, the wavelet hidden nodes are recursively selected from candidate node set to be added into WNN and the optimal parameters of selected nodes are obtained through extended Kalman filter (EKF).The optimum experimental design and Laplacian regularization are then integrated to select salient WNN hidden nodes, and minimum description length (MDL)is utilized to determine the number of hidden nodes.Initial WNN parameters and associated weight updating scheme are provided via an online Gustafson-kesscl (GK)based fuzzy satisfactory clustering algorithm with intuitive interpretation and physic meaning.Fi-nally,a predictive functional control law is given by linearizing WNN.The simulation of industrial coking equipment shows the efficiency of the proposed algorithm.关键词
小波神经网络/扩展卡尔曼滤波/预测控制/Laplacian正则化/满意模糊聚类Key words
wavelet neural networks (WNN)/extended Kalman filter/predictive control/Laplacian reg-ularization/satisfactory fuzzy clustering分类
信息技术与安全科学引用本文复制引用
任世锦,王高峰,李新玉,杨茂云,徐桂云..最优实验设计与Laplacian正则化的WNN的非线性预测控制[J].数据采集与处理,2016,31(5):927-940,14.基金项目
国家自然科学基金(60974056)资助项目。 （60974056）
