The use of trapezoidal function in a linguistic fuzzy relational neural network for speech recognition

Describes the use of trapezoidal functions as part of the implementation of a fuzzy relational neural network model. In the model, the input features are represented by their respective fuzzy membership values to linguistic properties. The membership values are calculated with trapezoidal functions. The weights of the connections between input and output nodes are described in terms of their fuzzy relations. The output values during training are obtained with the max-min composition, and are given in terms of fuzzy class membership values. The learning algorithm, used is a modified version of the gradient descent backpropagation algorithm. The classification of unknown patterns is made with the relational square product. The system is tested on a speech recognition problem.<
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