Add to ORKGAn algorithm is presented that uses evolutionary programming to construct fuzzy membership functions that are used to extract Zadeh-Mamdani fuzzy rules from a constructive neural network. The algorithm has potential applications in fields such as data mining and knowledge-based decision support systems. Evaluation of the algorithm over two well known benchmark data sets shows that while the results are promising, some problems are apparent. These problems provide avenues for further research.Michael J. Wattshttp://www.intjit.org/journal/volume/11/10/editorial.htm
This paper explores different techniques for extracting propositional rules from linguistic rule neu...
Research has been conducted on how to develop machine intelligence. Artificial Neural Networks (ANN)...
A method for extracting Zadeh–Mamdani fuzzy rules from a minimalist constructive neural network mode...
This paper discusses the question how the membership functions in a fuzzy rule based system can be e...
Fuzzy neural networks provide for the extraction of fuzzy rules from artificial neural network archi...
This paper presents a new approach to acquisition of comprehensible fuzzy rules for fuzzy modeling f...
In previous works, we have presented two methodologies to obtain fuzzy rules in order to describe th...
Artificial neural networks (ANN) have the ability to model input-output relationships from processin...
Artificial neural networks (ANNs) are mathematical models inspired from the biological nervous syste...
Abstract- Various rule-extraction techniques using ANNs have been used so far, most of them being ap...
Identification of fuzzy rules is an important issue in designing of a fuzzy neural network (FNN). H...
In essence, data mining consists of extracting knowledge from data. This paper proposes a co-evoluti...
Artificial neural network (ANN) is one of the most widely used techniques in classification data min...
Title: Artificial neural networks for clustering and rule extraction Author: Jiří Iša Department: De...
summary:The extraction of logical rules from data has been, for nearly fifteen years, a key applicat...
This paper explores different techniques for extracting propositional rules from linguistic rule neu...
Research has been conducted on how to develop machine intelligence. Artificial Neural Networks (ANN)...
A method for extracting Zadeh–Mamdani fuzzy rules from a minimalist constructive neural network mode...
This paper discusses the question how the membership functions in a fuzzy rule based system can be e...
Fuzzy neural networks provide for the extraction of fuzzy rules from artificial neural network archi...
This paper presents a new approach to acquisition of comprehensible fuzzy rules for fuzzy modeling f...
In previous works, we have presented two methodologies to obtain fuzzy rules in order to describe th...
Artificial neural networks (ANN) have the ability to model input-output relationships from processin...
Artificial neural networks (ANNs) are mathematical models inspired from the biological nervous syste...
Abstract- Various rule-extraction techniques using ANNs have been used so far, most of them being ap...
Identification of fuzzy rules is an important issue in designing of a fuzzy neural network (FNN). H...
In essence, data mining consists of extracting knowledge from data. This paper proposes a co-evoluti...
Artificial neural network (ANN) is one of the most widely used techniques in classification data min...
Title: Artificial neural networks for clustering and rule extraction Author: Jiří Iša Department: De...
summary:The extraction of logical rules from data has been, for nearly fifteen years, a key applicat...
This paper explores different techniques for extracting propositional rules from linguistic rule neu...
Research has been conducted on how to develop machine intelligence. Artificial Neural Networks (ANN)...
A method for extracting Zadeh–Mamdani fuzzy rules from a minimalist constructive neural network mode...