A fuzzy modelling approach using hierarchical neural networks
A simple and effective fuzzy modelling approach is presented in this paper. A three-layer hierarchical clustering neural network is developed to build fuzzy rule-based models front numerical data. Differing from existing clustering-based methods, in this approach the structure identification of the fuzzy model is implemented on the basis of a class of subclusters created by a self-organising network instead of on raw data. By combined use of unsupervised and supervised learning, both structure identification and parameter optimisation of the fuzzy model can be carried out automatically. The simulation results show that the proposed method can provide good model structure for fuzzy modelling and has high computing efficiency.
fuzzy modelling fuzzy clustering neural fuzzy systems non-linear system identification