Recognizing Odor Mixtures Using Optimized Fuzzy Neural Network Through Genetic Algorithms

被引：1

作者：

Kusumoputro, Benyamin ^{[1
]}

Arsyad, Teguh P. ^{[1
]}

机构：

[1] Univ Indonesia, Fac Comp Sci, Depok Campus,POB 3443, Jakarta 10002, Indonesia

来源：

JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS | 2005年 / 9卷 / 03期

关键词：

odor recognition system; fuzzy-neuro system; genetic algorithms; neural structure optimization method; multilayer perceptron;

D O I：

10.20965/jaciii.2005.p0290

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Recognizing odor mixtures is rather difficult in artificial odor recognition system, especially when the number of sensors is limited. Classification is further hampered if the number of unlearned odor mixtures classes is increased. We developed a fuzzy-neuro multilayer perceptron as a pattern classifier and compared its recognition with that of the Probabilistic Neural Network and Back-propagation Neural Network. To enhance the recognition capability of the system, we then optimized fuzzy-neuro multilayer perceptron topology by deleting its weak weight connections using Genetic Algorithms. Experimental results show that the optimized fuzzy-neuro multilayer perceptron has the highest recognition in 18 classes of two-mixture odors with almost 98.2% when using hardware with 16 sensors, compared to 83.3% when using 8 sensors.

引用

页码：290 / 296

页数：7

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