A Multiple-Valued Immune Network and Its Applications
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E82-A No.6 pp.1102-1108
- Publication Date
- 1999/06/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Neural Networks
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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 -
English
