Learning Membership Functions for an Associative Fuzzy Neural Network

Some novel heuristic methods for automatically building triangular, trapezoidal, Gaussian and sigmoid membership functions are introduced, providing a way to model linear attributes as linguistic variables. The utilization of such functions in five different fashions in the context of an Associative Fuzzy Neural Network outperformed two existing methods. Also, these heuristic methods are suitable for being applied to other knowledge representation formalisms that use fuzzy sets.

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