Journal ArticleDOI
Adaptive learning algorithm in classification of fuzzy patterns An application to vowels in CNC context
TLDR
An adaptive algorithm for recognition of ill-defined patterns using weak representative points and single pattern training procedure is presented from the standpoint of fuzzy set theory.Abstract:
An adaptive algorithm for recognition of ill-defined patterns using weak representative points and single pattern training procedure is presented from the standpoint of fuzzy set theory. The method includes both supervised and non-supervised schemes, A non-adaptive algorithm with fixed reference and weight vectors is also described to describe the efficiency of the system's adaptiveness to a new input. This was implemented to machine recognition of vowel sounds of a number of speakers in Consonant-Vowel Nucleus-Consonant (CNC) context considering the first three vowel formants as input features. The decision of the machine is governed by the maximum value of fuzzy membership function. A recognition rate, particularly for weak initial representative vectors, was seen to be dependent on the sequence of incoming patterns. As the process of classification continued, the learned moan vectors approached their respective true values of the clusters. Again, once the optimum size of training set is obtained, the r...read more
Citations
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Book
A review of probabilistic, fuzzy, and neural models for pattern recognition
TL;DR: In this article, the basic ideas of and some synergisms between probabilistic, fuzzy, and computational neural networks models as they apply to pattern recognition are discussed and a brief discussion of the relationship of both approaches to statistical pattern recognition methodologies is provided.
Journal ArticleDOI
A Review of Probabilistic, Fuzzy, and Neural Models for Pattern Recognition
TL;DR: The basic ideas of and some synergisms between probabilistic, fuzzy, and computational neural networks models as they apply to pattern recognition are discussed.
Book ChapterDOI
Research Guide to Applications of Fuzzy Set Theory in Human Factors
TL;DR: The literature related to application of fuzzy set theory to Human Factors and has 784 research papers listed in the bibliography which is given at the end is presented in this article, with a subjective viewpoint and may be in some sense incomplete.
Journal ArticleDOI
A self-supervised vowel recognition system
TL;DR: An adaptive model for computer recognition of vowel sounds with the first three formants as features using a single pattern training procedure for self-supervised learning and maximum value of fuzzy membership function is the basis of recognition.
Optimum guard zone for self-supervised learning
TL;DR: A self-supervised learning algorithm using fuzzy set and the concept of guard zones around the class representative vectors is presented and demonstrated for vowel recognition and an optimum guard zone having the best match with the fully supervised performance is determined.
References
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Journal ArticleDOI
Probability measures of Fuzzy events
TL;DR: In probability theory, an event, A, is a member of a a-field, CY, of subsets of a sample space ~2, where CY is any collection of disjoint events.
Journal ArticleDOI
Fuzzy sets and their applications to cognitive and decision processes
TL;DR: By providing a basis for a systematic approach to approximate reasoning, the theory of fuzzy sets may well have a substantial impact on scientific methodology in the years ahead, particularly in the realms of psychology, economics, law, medicine, decision analysis, information retrieval, and artificial intelligence.
Book
Fuzzy sets and their applications to cognitive and decision processes
TL;DR: Fuzzy sets are a class in which there may be a continuum of grades of membership as, say, in the class of long objects as mentioned in this paper, which underlie much of our ability to summarize, communicate, and make decisions under uncertainty or partial information.
Book
Computer-oriented approaches to pattern recognition
TL;DR: What do you do to start reading computer oriented approaches to pattern recognition?