Development of Interpretable Models through Neuro-Fuzzy Networks



In this paper a methodology for development of linguistically interpretable fuzzy models is presented. 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 extraction 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 described imposes some restrictions on the tuning of parameters and performs membership function merging. In this way, it will be easy to assign linguistic labels to each of the membership functions used. Therefore, the model obtained for the system under analysis will be described by a set of linguistic rules, easily interpretable.


system identification, fuzzy systems, neuro-fuzzy networks, clustering, interpretability


Neuro-Fuzzy Modelling


EIS'2000, June 2000

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