Design of adaptive fuzzy model for classification problem
| Autor: | Nai Ren Guo, Tzuu-Hseng S. Li, Chao-Lin Kuo |
|---|---|
| Rok vydání: | 2005 |
| Předmět: |
Adaptive neuro fuzzy inference system
Fuzzy classification Neuro-fuzzy Computer science business.industry Fuzzy set Fuzzy control system computer.software_genre Fuzzy logic Defuzzification ComputingMethodologies_PATTERNRECOGNITION Artificial Intelligence Control and Systems Engineering Fuzzy set operations Fuzzy number Fuzzy associative matrix Data mining Artificial intelligence Electrical and Electronic Engineering business computer Membership function |
| Zdroj: | Engineering Applications of Artificial Intelligence. 18:297-306 |
| ISSN: | 0952-1976 |
| DOI: | 10.1016/j.engappai.2004.09.011 |
| Popis: | The main theme of this paper is to set up an adaptive fuzzy model for a new classification problem. At first, we propose a fuzzy classification model that can automatically generate the fuzzy IF-THEN rules by the features of the training database. The consequent part of the fuzzy IF-THEN rule consists of the confident value of the rule and which class the datum should belong to. Then a novel adaptive modification algorithm (AMA) is developed to tune the confident value of the fuzzy classification model. The proposed model comprises three modules, generation of the fuzzy IF-THEN rules, determination of the classification unit, and setup of the AMA. Computer simulations on the well known Wine and Iris databases have tested the performance. Simulations demonstrate that the proposed method can provide sufficiently high classification rate in comparison with other fuzzy classification models. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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