In this paper, the McCulloch-Pitts model of a neuron is extended to a more general model which allows the activity of a neuron to be a "fuzzy" rather than an "all-or-none" process. The generalized model is called a fuzzy neuron. Some basic properties of fuzzy neural networks as well as their applications to the synthesis of fuzzy automata are investigated. It is shown that any n-state minimal fuzzy automatan can be realized by a network of m fuzzy neurons, where ⌈log2n⌉
CITATION STYLE
Lee, S. C., & Lee, E. T. (1975). Fuzzy Neural Networks. Mathematical Biosciences, 23(1–2), 151–177. https://doi.org/10.1016/0025-5564(75)90125-X
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