Symbolic Interpretation of Artificial Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Extracting Interpretable Fuzzy Rules from RBF Networks
Neural Processing Letters
Rule-based modeling: precision and transparency
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Simplification of fuzzy-neural systems using similarity analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
Interpretation of artificial neural networks by means of fuzzy rules
IEEE Transactions on Neural Networks
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In this paper the knowledge extraction from neural and fuzzy models, and the quality and the explanation capacities of this knowledge are tracked. Nowadays the application of algorithms and methodologies based on artificial neural networks and fuzzy logic are very usual in most of the scientific and technical areas in order to generate models driven by data. But sometimes to obtain a good model by these techniques is not enough when some explanations about the model behaviour are mandatory, and these models are very near, most of the cases, to ”black boxes” or its explanatory capacity is very poor. In literature, several methods are been published in order to extraction, simplification and interpretability of the knowledge stored in these types of models. In this paper a real problem is involved: to model an AC motor on several functioning modes (faults) by several neural/fuzzy approaches, making a comparison on the knowledge extracted from each one: Feedforward network + Backpropagation, Substractive Clustering + ANFIS, FasArt and FasBack.