Neural networks: algorithms, applications, and programming techniques
Neural networks: algorithms, applications, and programming techniques
A new approach of neuro-fuzzy learning algorithm for tuning fuzzy rules
Fuzzy Sets and Systems
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Essentials of Fuzzy Modeling and Control
Essentials of Fuzzy Modeling and Control
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Journal of Robotic Systems
A methodology to design stable nonlinear fuzzy control systems
Fuzzy Sets and Systems
Fuzzy sets in pattern recognition and machine intelligence
Fuzzy Sets and Systems
Designing fuzzy inference systems from data: An interpretability-oriented review
IEEE Transactions on Fuzzy Systems
The equivalence between fuzzy logic systems and feedforward neural networks
IEEE Transactions on Neural Networks
On reducing computational overhead in multi-objective genetic Takagi-Sugeno fuzzy systems
Applied Soft Computing
A genetic reduction of feature space in the design of fuzzy models
Applied Soft Computing
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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.