A Fuzzy Neuron with Binary Input and its Training Algorithm

摘要

This paper is concerned with a proposal for a fuzzy artificial neuron with bi-nary input. The fuzzy neuron is based on fuzzy logic in that each component of the input vector is compared to a number which represent the membership value for a 0 in that position. The results of the comparisons are then combined using a generalized mean function to produce a single number which is compared to a threshold as in the case of a perceptron consisting of a linear combiner with hard limiting function. A training algorithm is developed based on an algorithm for linear inequalities described by Ho and Kashyap in a paper titled ‘An Algorithm for Linear Inequalities and its Applications’. The results obtained by simulation look promising.