Abstract
In the 1950s, the mathematically oriented electrical engineer, Lotfi A. Zadeh, investigated system theory, and in the mid-1960s, he established the theory of Fuzzy sets and systems based on the mathematical theorem of linear separability and the pattern classification problem. Contemporaneously, the psychologist, Frank Rosenblatt, developed the theory of the perceptron as a pattern recognition machine based on the starting research in so-called artificial intelligence, and especially in research on artificial neural networks, until the book of Marvin L. Minsky and Seymour Papert disrupted this research program. In the 1980s, the Parallel Distributed Processing research group requickened the artificial neural network technology. In this paper, we present the interwoven historical developments of the two mathematical theories which opened up into fuzzy pattern classification and fuzzy clustering.
Highlights
In the 1950s, the mathematically oriented electrical engineer, Lotfi A
“At the lowest level, general pattern recognition reduces to pattern classification, which consists of techniques to separate groups of objects, sounds, odors, events, or properties into classes, based on measurements made on the entities being classified”
This said artificial intelligence (AI) pioneer, Charles Rosen, in the introduction of an article in Science in 1967, he claimed in the summary: “This function, pattern recognition, has become a major focus of research by scientists working in the field of artificial intelligence” [1] (p. 38, 43)
Summary
“Man’s pattern recognition process—that is, his ability to select, classify, and abstract significant information from the sea of sensory information in which he is immersed—is a vital part of his intelligent behavior.”. “At the lowest level, general pattern recognition reduces to pattern classification, which consists of techniques to separate groups of objects, sounds, odors, events, or properties into classes, based on measurements made on the entities being classified” This said artificial intelligence (AI) pioneer, Charles Rosen, in the introduction of an article in Science in 1967, he claimed in the summary: “This function, pattern recognition, has become a major focus of research by scientists working in the field of artificial intelligence” [1] Laboratory demonstrated to the public their experimental pattern recognition machine, the “Mark I perceptron” Another historical link connects pattern recognition or classification with the concept of linear separability when Minsky and Seymour Papert showed in their book, “Perceptrons: an introduction to computational geometry” published in 1969, that Rosenblatt’s perceptron was only capable of learning linearly separable patterns. Zadeh’s mathematically oriented thinking brought him to fundamental research in logics and statistics, and the wide spectrum of his interests in engineering sciences acquainted him with research on artificial neural networks and natural brains as well
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