An alternative approach for generation of membership functions and fuzzy rules based on radial and cubic basis function networks
TLDR
A novel method for the generation of fuzzy classification systems based on radial basis function networks with restricted Coulomb energy learning is presented, modified for easy hardware implementation by introducing cubic basis functions.About:
This article is published in International Journal of Approximate Reasoning.The article was published on 1995-04-01 and is currently open access. It has received 36 citations till now. The article focuses on the topics: Fuzzy classification & Fuzzy number.read more
Citations
More filters
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
Alternating cluster estimation: a new tool for clustering and function approximation
TL;DR: Out of a large variety of possible instances of non-AO models, an algorithm with a dynamically changing prototype function that extracts representative data and a computationally efficient algorithm with hyperconic membership functions that allows easy extraction of membership functions are presented.
Journal ArticleDOI
Improving the performance of fuzzy classifier systems for pattern classification problems with continuous attributes
Hisao Ishibuchi,T. Nakaskima +1 more
TL;DR: This paper describes a simple fuzzy classifiers system where a randomly generated initial population of fuzzy if-then rules is evolved by typical genetic operations, such as selection, crossover, and mutation, and introduces two heuristic procedures for improving the performance of the fuzzy classifier system.
Journal ArticleDOI
Computer-aided design of fuzzy systems based on generic VHDL specifications
TL;DR: In this paper, three types of fuzzy systems and related hardware architectures are discussed: standard fuzzy controllers, FuNe I fuzzy systems, and fuzzy classifiers based on a neural network structure.
References
More filters
Book
Fuzzy sets
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
MonographDOI
Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations
Journal ArticleDOI
On Estimation of a Probability Density Function and Mode
TL;DR: In this paper, the problem of the estimation of a probability density function and of determining the mode of the probability function is discussed. Only estimates which are consistent and asymptotically normal are constructed.
Book
Fuzzy Set Theory - and Its Applications
TL;DR: The book updates the research agenda with chapters on possibility theory, fuzzy logic and approximate reasoning, expert systems, fuzzy control, fuzzy data analysis, decision making and fuzzy set models in operations research.
Related Papers (5)
Neural networks in designing fuzzy systems for real world applications
Fuzzy neural networks: between functional equivalence and applicability.
Fuzzy identification of systems and its applications to modeling and control
T. Takagi,Michio Sugeno +1 more