最优实验设计与Laplacian正则化的WNN的非线性预测控制

摘要

提出了基于最优实验设计与Laplacian正则化的自适应小波神经网络(Wavelet neural network, WNN)的非线性预测控制算法。该方法迭代地从 WNN 隐含节点候选集选取隐含小波神经元,并使用扩展卡尔曼滤波(Extended Kalman filter,EKF)方法调整该节点参数。为了控制 WNN的复杂度,提出采用 Laplacian正则化和最优实验设计选择重要的 WNN 隐含节点,使用最小描述长度(Minimum de-scription length,MDL)准则确定节点数量。使用在线基于 Gustafson-kesscl(GK)的模糊满意聚类算法确定 WNN 初始参数值和权重更新策略,该策略具有直观性和物理意义。最后给出基于 WNN 线性化模型的预测函数控制方法。对工业焦化装置温度控制进行仿真,结果说明了算法的有效性。%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.