Generalized neural networks for fuzzy modeling
نویسندگان
چکیده
Neuro-fuzzy modeling has been intensively studied since the early nineties. Recently a method has been disclosed, that uses a classical feedforward neural network with just one hidden layer. Nodes of the hidden layer use the logistic function as activation function meanwhile the output node has a linear activation function. This paper introduces a generalization of the logistic function and evaluates its capabilities with respect to neuro-fuzzy modeling. It is shown that a product-generated symmetric summation provides an exact interpretation of the activity of each node of the hidden layer.
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