A fuzzy-similarity-based self-organized network inspired by immune algorithm for three-mixture-fragrance recognition

Widyanto, M. Rahmat; Kusumoputro, Benyamin; Nobuhara, Hajime; Kawamoto, Kazuhiko; Hirota, Kaoru

doi:10.1109/tie.2005.862212

Cited by 9 publications

(9 citation statements)

References 8 publications

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“…The artificial odor discrimination system consists of three subsystems, i.e., a sensory system, a frequency counter system, and a neural network (where F-SONIA [18] is used) as a pattern classifier system. The sensory system and the frequency counter system are used to measure frequency changes during data acquisition, and the pattern classifier system is used to discriminate odor characteristics obtained by the other systems.…”

Section: System Diagrammentioning

confidence: 99%

“…To deal with the uncertainty in frequency measurements, the minimum, the mean and the maximum value of samplings during data acquisition are used to form triangular fuzzy numbers. The fuzzy numbers become inputs for the previously developed F-SONIA [18]. Therefore, input data for the neural network is eight-dimensional .…”

Section: System Diagrammentioning

confidence: 99%

“…The F-SONIA [18] is an improvement of Self-Organized Network inspired by Immune Algorithm (SONIA) [21]. SONIA itself has been developed based on Back-Propagation Neural Network (BPNN) [22] with a hidden unit self-organization process inspired by immune algorithm [23].…”

Section: Limitations Of F-soniamentioning

confidence: 99%

“…In recent development of artificial odor discrimination system, a system that can deal with uncertainty of sensor array has been proposed [18]. The system proposed a pattern classification technique called Fuzzy similaritybased Self-Organized Network inspired by Immune Algorithm (F-SONIA).…”

Section: Introductionmentioning

confidence: 99%

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Unknown odor recognition using Euclidean Fuzzy similarity-based Self-Organized Network inspired by Immune Algorithm

Widyanto

Kusumoputro

Hirota

2007

Neural Comput & Applic

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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.

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Section: System Diagrammentioning

confidence: 99%

Section: System Diagrammentioning

confidence: 99%

Section: Limitations Of F-soniamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Unknown odor recognition using Euclidean Fuzzy similarity-based Self-Organized Network inspired by Immune Algorithm

Widyanto

Kusumoputro

Hirota

2007

Neural Comput & Applic

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“…To speed up the convergence of each antibody and reduce the computational effort necessary to simulate the whole population, a cluster and gradient based AIS was proposed in [18]. Other attempts to develop AIS in optimization scenarios are made to comCopyright c 2015 The Institute of Electronics, Information and Communication Engineers bine AIS with one of the other intelligent algorithms, such as simulated annealing [24], [25], ant colony optimization [26], genetic algorithm [27], fuzzy self-organized network [28], particle swarm optimization [29], and so on.…”

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confidence: 99%

A Multi-Learning Immune Algorithm for Numerical Optimization

Wang

Gao

Aorigele

et al. 2015

IEICE Trans. Fundamentals

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SUMMARYThe emergence of nature-inspired algorithms (NIA) is a great milestone in the field of computational intelligence community. As one of the NIAs, the artificial immune algorithm (AIS) mimics the principles of the biological immune system, and has exhibited its effectiveness, implicit parallelism, flexibility and applicability when solving various engineering problems. Nevertheless, AIS still suffers from the issues of evolution premature, local minima trapping and slow convergence due to its inherent stochastic search dynamics. Much effort has been made to improve the search performance of AIS from different aspects, such as population diversity maintenance, adaptive parameter control, etc. In this paper, we propose a novel multi-learning operator into the AIS to further enrich the search dynamics of the algorithm. A framework of embedding multiple commonly used mutation operators into the antibody evolution procedure is also established. Four distinct learning operators including baldwinian learning, cauchy mutation, gaussian mutation and lateral mutation are selected to merge together as a multi-learning operator. It can be expected that the multi-learning operator can effectively balance the exploration and exploitation of the search by enriched dynamics. To verify its performance, the proposed algorithm, which is called multi-learning immune algorithm (MLIA), is applied on a number of benchmark functions. Experimental results demonstrate the superiority of the proposed algorithm in terms of convergence speed and solution quality.

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