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To deal with unknown odor recognition problem for a developed artificial odor discrimination system, Euclidean Fuzzy similarity-based Self-Organized Network inspired by Immune Algorithm (EF-SONIA) is proposed. Euclidean fuzzy similarity enables a zero similarity calculation between an unknown odor vector and hidden unit vectors, so that the system can recognize the unknown odor. In addition, an elliptical approach for fuzziness determination is proposed. The elliptical approach can approximate an appropriate fuzziness, so that the unknown odor recognition accuracy is improved. Experiments on three datasets of three-mixture vegetal odors show that the recognition accuracy of the proposed method is 20% better than those of the conventional method. The system is very promising to be used for a real development of dog robot that enables localization and identification of dangerous natural gas. [ABSTRACT FROM AUTHOR]

To deal with a problem of overlapping data in pattern classification, a class majority method in designing hidden units of Fuzzy Local Approximation NN is proposed. Moreover, to improve the output confidence of the networks, Euclidean fuzzy similarity is proposed as hidden unit operator. For each cluster formed by the adaptive clustering of F-SONIA, the number of vectors that belong to the same class is calculated. Therefore the fractions of each class are known. One class should have a single class majority in the cluster. Then, the cluster with no single majority is broken down into two clusters. In experiments, the real-world benchmark datasets, e.g., 2-D vowel, Iris, and thyroid data that have different challenges to the networks in terms of overlapping and size are used to test the networks. Experiments show that the proposed methods improve the classification performance as well as the output confidence of the networks. [ABSTRACT FROM AUTHOR]