New approaches on structure identification of fuzzy models: Case study in an electro-mechanical system

Abstract

The main problem in design fuzzy models is to identify their structure. This means recognise the variables that better characterise the system dynamics, the number of membership functions partitioning each variable, as well as their distribution and fuzziness degree. This work presents two pre-processing methods for structure identification of fuzzy models. The first approach uses the statistical method of Principal Component Analysis (PCA). The second one uses a clustering technique called autonomous mountain-clustering method. The statistical method of Principal Component Analysis helps to select the variables that dominate the system dynamics. Besides, this method contributes to design fuzzy models with better performance. The second approach identifies the fuzzy model order. That is, the method identifies the number of membership functions attributed to each variable, as well as their position and width. So, the autonomous mountain-clustering eliminates the usual “trial-and-error” mechanism. The pre-processing methods can be used to initialize the neuro-fuzzy techniques and therefore accelerate their learning process. We test these methods using a simple learning process applied to extract the fuzzy model of an experimental electrohydraulic system. The results show that a good modeling capability is achieved without employ any complicated optimisation procedure to structure identification.