Showing papers on "Neuro-fuzzy published in 1975"
TL;DR: The McCulloch-Pitts model of a neuron is extended to a more general model which allows the activity of a neurons to be a “fuzzy” rather than an “all-or-none” process, called a fuzzy neuron.
Abstract: 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⌉
214 citations
01 Jan 1975
TL;DR: The chapter presents a computer simulation of the process of human learning by making use of the concept of fuzzy program and learning algorithm.
Abstract: Publisher Summary This chapter discusses several methods that translate a given sequence of fuzzy instructions into another sequence of precise instructions called a machine program. A finite-state automaton is taken up as a fuzzy machine model that executes a fuzzy program. The chapter presents the formulation of an extended fuzzy machine based on a generalized automaton and a few procedures for execution of fuzzy programs. An L-fuzzy automaton with the weight space defined in the lattice ordered semigroup is considered as a general machine. Several machines are also derived from L-fuzzy automata as their specific examples. The chapter discusses a more general way of executing fuzzy programs by making use of the generalized fuzzy machine. The chapter presents a computer simulation of the process of human learning by making use of the concept of fuzzy program and learning algorithm.
29 citations
13 citations
01 Jan 1975
6 citations
01 Jan 1975
TL;DR: The objective of this chapter will be to show how much of fuzzy systems theory goes through in this context and then to see how finiteness is reflected in the dynamics.
Abstract: In this chapter we introduce the concept of a fuzzy automaton in such a way as to make it clear that every finite relational fuzzy system is a fuzzy automaton. Our objective will then be to show how much of fuzzy systems theory goes through in this context and then to see how is finiteness reflected in the dynamics. Because of their finiteness, most of the problems concerning fuzzy automata are better understood. Turning this insinuation into formal theorems requires the use of matrix theory.