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INTEGRATED ADAPTIVE FUZZY CLUSTERING (IAFC) NEURAL NETWORKS USING FUZZY LEARNING RULES | ||
| Iranian Journal of Fuzzy Systems | ||
| مقاله 2، دوره 2، شماره 2، دی 2005، صفحه 1-13 اصل مقاله (195.17 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22111/ijfs.2005.477 | ||
| نویسندگان | ||
| YONG SOO KIM* 1؛ Z. ZENN BIEN2 | ||
| 1DIVISION OF COMPUTER ENGINEERING, DAEJEON UNIVERSITY, DAEJEON, 300-716, KOREA | ||
| 2DEPARTMENT OF ELECRICAL ENGINEERING AND COMPUTER SCIENCE, KAIST, DAEJEON, 305-701, KOREA | ||
| چکیده | ||
| The proposed IAFC neural networks have both stability and plasticity because they use a control structure similar to that of the ART-1(Adaptive Resonance Theory) neural network. The unsupervised IAFC neural network is the unsupervised neural network which uses the fuzzy leaky learning rule. This fuzzy leaky learning rule controls the updating amounts by fuzzy membership values. The supervised IAFC neural networks are the supervised neural networks which use the fuzzified versions of Learning Vector Quantization (LVQ). In this paper, several important adaptive learning algorithms are compared from the viewpoint of structure and learning rule. The performances of several adaptive learning algorithms are compared using Iris data set. | ||
| کلیدواژهها | ||
| Neural Networks؛ Fuzzy logic؛ Fuzzy neural networks؛ Learning rule؛ Fuzzification | ||
| مراجع | ||
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