A New Rule Selection Procedure for Fuzzy-Neural Modelling

Abstract In identification of complex dynamic systems using fuzzy neural networks, one of the main issues is the curse-of-dimensionality. In the literature this issue has been addressed by the selection of either the inputs or the rules. Adding unnecessary inputs or rules increases the model complexity and worsens the network generalization performance. Selecting the best set of inputs or rules is a combinational problem and can be computationally expensive. In this paper, the problem is solved by first proposing a refinement procedure for rule selection. The algorithm is then adapted with prior input selection to further improve the model accuracy.