A method for fuzzy rules extraction directly from numerical data and its application to pattern classification
Shigeo Abe,Ming-Shong Lan +1 more
TLDR
A new method for extracting fuzzy rules directly from numerical input-output data for pattern classification by recursively resolving overlaps between two classes is discussed.Abstract:
In this paper, we discuss a new method for extracting fuzzy rules directly from numerical input-output data for pattern classification Fuzzy rules with variable fuzzy regions are defined by activation hyperboxes which show the existence region of data for a class and inhibition hyperboxes which inhibit the existence of data for that class These rules are extracted from numerical data by recursively resolving overlaps between two classes Then, optimal input variables for the rules are determined using the number of extracted rules as a criterion The method is compared with neural networks using the Fisher iris data and a license plate recognition system for various examples >read more
Citations
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Control of Multifunctional Prosthetic Hands by Processing the Electromyographic Signal
TL;DR: The traditional methods used to control artificial hands by means of EMG signal are presented, in both the clinical and research contexts, and what could be the future developments in the control strategy of these devices are introduced.
Proceedings Article
Performance evaluation of fuzzy classfier systems for multi-dimensional pattern classification problems
TL;DR: A genetics-based machine learning method that automatically generates fuzzy if-then rules for pattern classification problems from numerical data that works very well in comparison with other classification methods such as nonfuzzy machine learning techniques and neural networks.
Journal ArticleDOI
Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems
TL;DR: In this article, a fuzzy genetics-based machine learning method for multidimensional pattern classification problems with continuous attributes is presented, where each fuzzy if-then rule is handled as an individual, and a fitness value is assigned to each rule.
Journal ArticleDOI
Effect of rule weights in fuzzy rule-based classification systems
TL;DR: Through computer simulations, it is shown that comprehensible fuzzy rule-based systems with high classification performance can be designed without modifying the membership functions of antecedent linguistic values when the authors use fuzzy IF-THEN rules with certainty grades.
References
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Book ChapterDOI
Learning internal representations by error propagation
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
Book
Learning internal representations by error propagation
TL;DR: In this paper, the problem of the generalized delta rule is discussed and the Generalized Delta Rule is applied to the simulation results of simulation results in terms of the generalized delta rule.
Journal ArticleDOI
Neural-network-based fuzzy logic control and decision system
Chin-Teng Lin,C.S.G. Lee +1 more
TL;DR: A general neural-network (connectionist) model for fuzzy logic control and decision systems is proposed, in the form of feedforward multilayer net, which avoids the rule-matching time of the inference engine in the traditional fuzzy logic system.
Journal ArticleDOI
Fuzzy min-max neural networks. I. Classification
TL;DR: The fuzzy min-max classifier neural network implementation is explained, the learning and recall algorithms are outlined, and several examples of operation demonstrate the strong qualities of this new neural network classifier.