Active noise control by an immune algorithm: adaptation in immune system as an evolution

Ishida, Yoshiteru; Adachi, Nobue

doi:10.1109/icec.1996.542351

Cited by 24 publications

(7 citation statements)

References 5 publications

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“…The TCRs cannot recognize antigens per se: they can recognize only binding between peptide antigens produced by antigen decomposition at B cells, and MHC-II molecules on the surface of B cells [3]. This is due to the discrimination between MHC-II molecules (self) and peptide antigens (nonself):…”

Section: The Immune Response Networkmentioning

confidence: 99%

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An immunity network with provision for diverse recognition in T‐cell receptors

Yamaguchi

Fukata

Aramaki

et al. 2009

Elect Comm in Japan

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SUMMARYRecently, biological superior information processing ability has been researched to be able to be useful for a computer system. In particular, neural networks for the brain and nervous system have been researched. Immunity network models based on the biological immune response network also have been studied, but most of the researchers consider only a basic part of the interaction between B cells and T cells. Recent research has reported that the function of T cell receptors (TCR) is important in the practical immune response network. Therefore, we propose a new immunity network model which, unlike the traditional immunity networks, has the same function as that of a TCR. We applied the proposed immunity network model and the traditional immunity network model to the pattern recognition system. And we compared the performance of the proposed immunity network model with the traditional immunity network model, and showed the usability of the proposed immunity network model.

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Section: The Immune Response Networkmentioning

confidence: 99%

“…There have been attempts to apply biological immune mechanisms to the autonomous behavior of robots [2], optimization [3], noise removal [4], and some other fields. In addition, there is research on pattern recognition using information processing based on the immune response in biological systems [5].…”

Section: Introductionmentioning

confidence: 99%

An immunity network with provision for diverse recognition in T‐cell receptors

Yamaguchi

Fukata

Aramaki

et al. 2009

Elect Comm in Japan

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“…Artificial immune system has been applied to fault detection, hacking, pattern recognition and many other areas [1,2,3] . In the use of artificial immune system in detection and identification, the most common method is to collect the abstract characteristics of things to be tested in the from of string, so the problem will be transformed to string recognition [4] .…”

Section: Introductionmentioning

confidence: 99%

Implementation of String Recognition Algorithm Based on the Principle of Artificial Immunology

Wang

Dong

et al. 2008

2008 the 9th International Conference for Young Computer Scientists

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Artificial immune system simulates the principle of lymphocytes in the natural immune system, detects abnormality in objects to be tested through the immune response mechanism. Based on the r-row-matching algorithm, we propose a two-stage r-row-matching algorithm. According to the concept of immunity, the immunity process and mechanism, we design self-set and detector-set by using a sliding window. On this basis, the self-set, the detector-set and other generating algorithm, as well as the algorithm of detector-set training are implemented. Finally, based on the data structure of sliding window, the string recognition algorithm is implemented. Experiments show that the algorithm can effectively carry out string identification.

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“…There have been attempts to apply the concept of immune systems to the autonomous behavior of robots [2], optimization [3], noise removal [4], and other problems. In addition, there is research on immune response functions in immune systems, aiming at pattern recognition using various immune reactions [5].…”

Section: Introductionmentioning

confidence: 99%

Multiple‐valued immune network with apoptosis system

Yamaguchi

Tang

2007

Electrical Engineering Japan

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SUMMARYIn this paper, we describe a new model of immune network based on biological immune response network. We propose an immunity like multiple-valued network with apoptosis mechanism. The model is based on the interaction between B cells and T cells and the biological apoptosis mechanism in the human body. With the mechanism, a naturally immune system can be reproduced. The model is also applied to pattern recognition. It becomes possible with a conventional model to restrict the category increase of memory patterns.

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Active noise control by an immune algorithm: adaptation in immune system as an evolution

Cited by 24 publications

References 5 publications

An immunity network with provision for diverse recognition in T‐cell receptors

An immunity network with provision for diverse recognition in T‐cell receptors

Implementation of String Recognition Algorithm Based on the Principle of Artificial Immunology

Multiple‐valued immune network with apoptosis system

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