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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Makalah ini menerangkan model baharu rangkaian imun berbilang nilai berdasarkan rangkaian tindak balas imun biologi. Model rangkaian imun berbilang nilai dirumuskan berdasarkan analogi dengan interaksi antara sel B dan sel T dalam sistem imun. Model ini mempunyai sifat yang menyerupai tindak balas imun dengan baik. Kekebalan rangkaian disimulasikan dan membuat beberapa ramalan yang boleh diuji secara eksperimen. Hasil simulasi diberikan kepada aplikasi pengecaman huruf rangkaian dan dibandingkan dengan yang binari. Simulasi menunjukkan bahawa, di samping kelebihan kategori yang kurang, corak ingatan yang lebih baik dan kapasiti ingatan yang baik, rangkaian imun berbilang nilai menghasilkan imuniti hingar yang lebih kuat daripada binari.
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Salinan
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
Salinan
@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},}
Salinan
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 -