A new approach for multimodel identification of complex systems based on both neural and fuzzy clustering algorithms

The multimodel approach was recently developed to deal with the issues of complex systems modeling and control. Despite its success in different fields, it is still faced with several design problems, in particular the determination of the number and parameters of the different models representative of the system as well as the choice of the adequate method of validities computation used for multimodel output deduction.In this paper, a new approach for complex systems modeling based on both neural and fuzzy clustering algorithms is proposed, which aims to derive different models describing the system in the whole operating domain. The implementation of this approach requires two main steps. The first step consists in determining the structure of the model-base. For this, the number of models must be firstly worked out by using a neural network and a Rival Penalized Competitive Learning (RPCL). The different operating clusters are then selected referring to two different clustering algorithms (K-means and fuzzy K-means). The second step is a parametric identification of the different models in the base by using the clustering results for model orders and parameters estimation. This step is ended in a validation procedure which aims to confirm the efficiency of the proposed modeling by using the adequate method of validity computation. The proposed approach is implemented and tested with two nonlinear systems. The obtained results turn out to be satisfactory and show a good precision, which is strongly related to the dispersion of the data and the related clustering method.