Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy architectures and hybrid learning
Neuro-fuzzy architectures and hybrid learning
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Foundations of Neuro-Fuzzy Systems
Foundations of Neuro-Fuzzy Systems
Fuzzy Classifier Design
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Presented paper shows a new approach to creating a fuzzy system based on an exclusive use of clustering algorithms, which determine the value of necessary parameters. The applied multisegment fuzzy system functions as a classifier. Each segment makes an independent fuzzy system with a defined knowledge base and uses singleton fuzzification, as well as fuzzy inference with product operation as the Cartesian product and well-matched membership functions. Defuzzification method is not used. Only the rule-firing level must be analysed and its value suffices to determine the class. The use of clustering algorithms has allowed a qualification of the number of rules in the base of fuzzy rules for each independent segment, as well as a specification of the centers of fuzzy sets used in the given rules. The calculated parameters have proved precise, so that no additional methods have been applied to correct their values. This procedure greatly simplifies the creation of a fuzzy system. The constructed fuzzy system has been tested on medical data that come from the Internet. In the future, those systems may help doctors with their everyday work.