The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Este artigo descreve um novo modelo de rede imunológica de múltiplos valores baseada na rede de resposta imune biológica. O modelo de rede imunológica de múltiplos valores é formulado com base na analogia com a interação entre células B e células T no sistema imunológico. O modelo tem uma propriedade que se assemelha muito bem à resposta imunológica. A imunidade da rede é simulada e faz várias previsões testáveis experimentalmente. Os resultados da simulação são fornecidos a uma aplicação de reconhecimento de letras da rede e comparados com os binários. As simulações mostram que, além das vantagens de menos categorias, melhor padrão de memória e boa capacidade de memória, a rede imunológica de valores múltiplos produz uma imunidade a ruído mais forte do que a binária.
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Zheng TANG, Takayuki YAMAGUCHI, Koichi TASHIMA, Okihiko ISHIZUKA, Koichi TANNO, "A Multiple-Valued Immune Network and Its Applications" in IEICE TRANSACTIONS on Fundamentals,
vol. E82-A, no. 6, pp. 1102-1108, June 1999, doi: .
Abstract: This paper describes a new model of multiple-valued immune network based on biological immune response network. The model of multiple-valued immune network is formulated based on the analogy with the interaction between B cells and T cells in immune system. The model has a property that resembles immune response quite well. The immunity of the network is simulated and makes several experimentally testable predictions. Simulation results are given to a letter recognition application of the network and compared with binary ones. The simulations show that, beside the advantages of less categories, improved memory pattern and good memory capacity, the multiple-valued immune network produces a stronger noise immunity than binary one.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e82-a_6_1102/_p
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@ARTICLE{e82-a_6_1102,
author={Zheng TANG, Takayuki YAMAGUCHI, Koichi TASHIMA, Okihiko ISHIZUKA, Koichi TANNO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Multiple-Valued Immune Network and Its Applications},
year={1999},
volume={E82-A},
number={6},
pages={1102-1108},
abstract={This paper describes a new model of multiple-valued immune network based on biological immune response network. The model of multiple-valued immune network is formulated based on the analogy with the interaction between B cells and T cells in immune system. The model has a property that resembles immune response quite well. The immunity of the network is simulated and makes several experimentally testable predictions. Simulation results are given to a letter recognition application of the network and compared with binary ones. The simulations show that, beside the advantages of less categories, improved memory pattern and good memory capacity, the multiple-valued immune network produces a stronger noise immunity than binary one.},
keywords={},
doi={},
ISSN={},
month={June},}
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TY - JOUR
TI - A Multiple-Valued Immune Network and Its Applications
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1102
EP - 1108
AU - Zheng TANG
AU - Takayuki YAMAGUCHI
AU - Koichi TASHIMA
AU - Okihiko ISHIZUKA
AU - Koichi TANNO
PY - 1999
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E82-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 1999
AB - This paper describes a new model of multiple-valued immune network based on biological immune response network. The model of multiple-valued immune network is formulated based on the analogy with the interaction between B cells and T cells in immune system. The model has a property that resembles immune response quite well. The immunity of the network is simulated and makes several experimentally testable predictions. Simulation results are given to a letter recognition application of the network and compared with binary ones. The simulations show that, beside the advantages of less categories, improved memory pattern and good memory capacity, the multiple-valued immune network produces a stronger noise immunity than binary one.
ER -