A fuzzy neural network algorithm for multigroup classification

We propose a multigroup classification algorithm based on a hybrid fuzzy neural net framework. A key feature of the approach is the adaptation of membership functions to new data. In this way, learning is reflected in the shape of the membership functions. By defining separate membership functions for each fuzzy output class, we allow dynamic adjustment of the functions during training. The algorithm is successfully tested with real economic data. The results suggest economically meaningful interpretations.

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