2026-03-11T12:49:54Z https://kanazawa-u.repo.nii.ac.jp/oai

oai:kanazawa-u.repo.nii.ac.jp:00008393 2024-06-20T06:13:47Z 934:935:936

A learning multiple-valued logic networkusing genetic algorithm Todo, Yuki Mitsui, Takahiro This paper describes a genetic algorithm based learning Multiple-Value Logic (MVL) network. The proposed learning network operates on a population of candidate window parameters to produce new window parameters with lower errors between the desired outputs and the actual outputs of the MVL network. Thus, the learning MVL network has a large number of search points, making it possible to obtain a global min- imum. The learning capability of the proposed MVL network with genetic algorithm is con rmed by simulations on several typical MVL functions. The simulation results show that the genetic algorithm based learning MVL network efficiently nds the appropriate network, window parameters, and bias, so that the MVL functions, especially for those relatively small problems. ICIC International / Inderscience 2014-04-01 2017-10-03 eng journal article AM http://hdl.handle.net/2297/36317 https://kanazawa-u.repo.nii.ac.jp/records/8393 AA12218449 1349-4198 International Journal of Innovative Computing, Information and Control 10 2 565 574 https://kanazawa-u.repo.nii.ac.jp/record/8393/files/TE-PR-TODO-Y-565.pdf application/pdf 545.2 kB 2017-10-03