A New Training Algorithm for a Fuzzy Perceptron and Its Convergence
نویسندگان
چکیده
In this paper, we present a new training algorithm for a fuzzy perceptron. In the case where the dimension of the input vectors is two and the training examples are separable, we can prove a finite convergence, i.e., the training procedure for the network weights will stop after finite steps. When the dimension is greater than two, stronger conditions are needed to guarantee the finite convergence.
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