Interpretability and Learning in Neuro-Fuzzy Systems



A methodology for development of linguistically interpretable fuzzy models from data is pre-sented. The implementation of the model is conducted through the training of a neuro fuzzy network, i.e., a neural net architecture capable of representing a fuzzy system. In the first phase the structure of the model is obtained by subtractive clustering, which allows the ex-traction of a set of relevant rules based on a set of representative input output data samples. In the second phase, the model parameters are tuned via the training of a neural network through backpropagation. In order to attain interpretability goals, the method proposed im-poses some restrictions on the tuning of parameters and performs membership function merg-ing. In this way, it will be easy to assign linguistic labels to each of the membership functions obtained after training. Therefore, the model obtained for the system under analysis will be described by a set of linguistic rules, easily interpretable.


system identification, fuzzy system models, neuro-fuzzy systems, clustering, interpretability, transparency


Neuro-Fuzzy Modelling


Fuzzy Sets and Systems, Vol. 147, #1, pp. 17-38, Elsevier, October 2004

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Year 2015 : 6 citations

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Year 2014 : 4 citations

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 Kolman E. and Margalioty M. (2007). “Knowledge Extraction from Neural Networks using the All-Permutations Fuzzy Rule Base: the LED display recognition problem”. IEEE Transactions on Neural Networks, Vol. 18, No. 3, pp. 925-931.

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Year 2005 : 7 citations

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