A new methodology to improve interpretability in neuro-fuzzy TSK models

The present paper puts forward a methodology which allows increasing interpretability of TSK models identified by means of neuro-fuzzy techniques, although it shall also be applicable to models identified through other hybrid or different techniques. With this purpose, this paper puts forward a method which allows oriented adjustment of the rules’ precedent and consequent parameters in TSK models. The methodology extends the adaptive phase with an adjustment phase (or fine tuning phase) based on overlap ratio and overlap area, where the gradient descendent algorithm is used to adjust precisely the adapted parameters in the fuzzy model. The adjustment based on the overlap ratio is applied to the parameters defining the rules’ precedent and consequent parts. The overlap area becomes a more precise tuning of parameters of precedent part of rules. After the adaptation of the neuro-fuzzy model by means of the developed methodology, the model acquires a clear physical meaning enabling its immediate linguistic interpretation. Finally, some examples are given to prove the validity of the developed methodology.