Based On Rough Sets And Clustering Neural Fuzzy Modeling

In the control of complicated modern industrial plants,because most of the plants have many input variables,high non-linearity,tight coupling,serious transmission lag,distributed parameters,time -varying and many kinds of disturbances,the conventional control methods based on the exact mathematical model can not achieve both approving dynamic and static results. Recently,Neuro-fuzzy Control base on the Neural Network Theory and Fuzzy Logic System was used widely and successfully. In most of these Neuro-fuzzy modeling methods,the ANFIS (Adaptive -Network-based Fuzzy Inference Systems) method which was proposed by Jang in 1993 is the most prominent one,its adaptive property made it possible to be used in adaptive control and learning control directly. In fact,it can replace any Neural Network of the control systems and carry out the same function. However,it has the same problem as most of the Neuro-fuzzy systems - rule explosion problem. It is a difficult problem relates to the structure identification of the system and concerns with the partition of the input and output space and the rule generation of the raw sample data.Rough set theory,introduced by Zdzislaw Pawlak in the early 1980s,is another new powerful mathematical tool to deal with vagueness and uncertainty. It can analyze the imprecise,inconsistent and incomplete information effectively,find out the connotative knowledge and detect the potential rule of the system under consideration.In this paper,combine with what the author learned and studied during the three years,using the Cluster Algorithm and the Rough Set Theory,the author propos two new rule generation methods and put forward two new Neuro-fuzzy modeling methods with the T-S type of rule consequent,which named SCANFIS and RSANFIS respectively. Both methods can get over the rule explosion problem and confine the size of the rule base to a reasonable one. They give effective solutions to the modeling of the complicate multi-input systems. The simulation results show that both methods are more powerful than ANFIS method in modeling complicate multi-input systems concerning with efficiency and precision of the model.