Zhang J, Morris A J
Centre for Process Analysis, Chemometrics and Control, Department of Chemical and Process Engineering, University of Newcastle, Newcastle upon Tyne, NE1 7RU, UK.
IEEE Trans Neural Netw. 1999;10(2):313-26. doi: 10.1109/72.750562.
A type of recurrent neuro-fuzzy network is proposed in this paper to build long-term prediction models for nonlinear processes. The process operation is partitioned into several fuzzy operating regions. Within each region, a local linear model is used to model the process. The global model output is obtained through the centre of gravity defuzzification which is essentially the interpolation of local model outputs. This modeling strategy utilizes both process knowledge and process input output data. Process knowledge is used to initially divide the process operation into several fuzzy operating regions and to set up the initial fuzzification layer weights. Process input output data are used to train the network. Network weights are such trained so that the long-term prediction errors are minimized. Through training, membership functions of fuzzy operating regions are refined and local models are learn. Based on the recurrent neuro-fuzzy network model, a novel type of nonlinear model-based long range predictive controller can be developed and it consists of several local linear model-based predictive controllers. Local controllers are constructed based on the corresponding local linear models and their outputs are combined to form a global control action by using their membership functions. This control strategy has the advantage that control actions can be calculated analytically avoiding the time consuming nonlinear programming procedures required in conventional nonlinear model-based predictive control. The techniques have been successfully applied to the modeling and control of a neutralization process.
本文提出了一种递归神经模糊网络，用于构建非线性过程的长期预测模型。将过程操作划分为若干模糊操作区域。在每个区域内，使用局部线性模型对过程进行建模。全局模型输出通过重心去模糊化获得，这本质上是局部模型输出的插值。这种建模策略利用了过程知识和过程输入输出数据。过程知识用于最初将过程操作划分为若干模糊操作区域，并设置初始模糊化层权重。过程输入输出数据用于训练网络。网络权重经过训练，以使长期预测误差最小化。通过训练，模糊操作区域的隶属函数得到细化，局部模型得以学习。基于递归神经模糊网络模型，可以开发一种新型的基于非线性模型的远程预测控制器，它由几个基于局部线性模型的预测控制器组成。局部控制器基于相应的局部线性模型构建，其输出通过隶属函数组合形成全局控制作用。这种控制策略的优点是可以解析计算控制作用，避免了传统基于非线性模型的预测控制中所需的耗时非线性规划程序。这些技术已成功应用于中和过程的建模和控制。
