Construction of a neuron-fuzzy classification model based on feature-extraction approach
| Autor: | Nai Ren Guo, Tzuu-Hseng S. Li |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
Adaptive neuro fuzzy inference system
Fuzzy classification Neuro-fuzzy business.industry Fuzzy set Feature extraction General Engineering Pattern recognition computer.software_genre Defuzzification Computer Science Applications Artificial Intelligence Fuzzy number Fuzzy set operations Artificial intelligence Data mining business computer Mathematics |
| Zdroj: | Expert Systems with Applications. 38:682-691 |
| ISSN: | 0957-4174 |
| DOI: | 10.1016/j.eswa.2010.07.020 |
| Popis: | In this paper, a Feature-Extraction Neuron-Fuzzy Classification Model (FENFCM) is proposed that enables the extraction of feature variables and provides the classification results. The proposed classification model synergistically integrates a standard fuzzy inference system and a neural network with supervised learning. The FENFCM automatically generates the fuzzy rules from the numerical data and triangular functions that are used as membership functions both in the feature extraction unit and in the inference unit. To adapt the proposed FENFCM, two modificatory algorithms are applied. First, we utilize Evolutionary Programming (EP) to determine the distribution of fuzzy sets for each feature variable of the feature extraction unit. Second, the Weight Revised Algorithm (WRA) is used to regulate the weight grade of the principal output node of the inference unit. Finally, the proposed FENFCM is validated using two benchmark data sets: the Wine database and the Iris database. Computer simulation results demonstrate that the proposed classification model can provide a sufficiently high classification rate in comparison with that of other models proposed in the literature. |
| Databáze: | OpenAIRE |
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