Fuzzy bounded least squares method for systems identification

This paper presents the fuzzy bounded least squares method which uses both linguistic information and numerical data to identify linear systems. This method introduces a new type of fuzzy system, i.e., a fuzzy interval system. The steps in the method are: first, to utilize all the available linguistic information to obtain a fuzzy interval system and then to use the fuzzy interval system to give the admissible model set (i.e., the set of all models which are acceptable and reasonable from the point of view of linguistic information). Second, to find a model in the admissible model set which best fits the available numerical data. It is shown in the paper that such a model can be obtained by a quadratic programming approach. By comparing this method with the least squares method, it is proved that the model obtained by this method fits a real system better than the model obtained by the least squares method. In addition, this method also checks the adequacy of linear models for modelling a given system during the identification process and can help one to decide whether it is necessary to use nonlinear models