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
Kertas kerja ini mencadangkan algoritma pembelajaran pemodelan kebarangkalian untuk pendekatan carian tempatan kepada rangkaian Logik Bernilai Berbilang (MVL). Model pembelajaran (PMLS) mempunyai dua fasa: fasa carian tempatan (LS) dan fasa model probabilistik (PM). LS melakukan carian dengan mengemas kini parameter rangkaian MVL. Ia bersamaan dengan penurunan kecerunan ukuran ralat, dan membawa kepada ralat minimum setempat yang mewakili penyelesaian yang baik kepada masalah tersebut. Setelah LS terperangkap dalam minima tempatan, fasa PM cuba menjana titik permulaan baharu untuk LS untuk carian selanjutnya. Dijangkakan bahawa pencarian selanjutnya dipandu ke kawasan yang menjanjikan oleh model kebarangkalian. Oleh itu, algoritma yang dicadangkan boleh melarikan diri dari minima tempatan dan mencari hasil yang lebih baik lagi. Kami menguji algoritma pada banyak rangkaian MVL yang dijana secara rawak. Hasil simulasi menunjukkan bahawa algoritma yang dicadangkan adalah lebih baik daripada kaedah pembelajaran carian tempatan yang dipertingkatkan yang lain, seperti carian tempatan dinamik stokastik (SDLS) dan carian tempatan dinamik huru-hara (CDLS).
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Salinan
Shangce GAO, Qiping CAO, Masahiro ISHII, Zheng TANG, "Local Search with Probabilistic Modeling for Learning Multiple-Valued Logic Networks" in IEICE TRANSACTIONS on Fundamentals,
vol. E94-A, no. 2, pp. 795-805, February 2011, doi: 10.1587/transfun.E94.A.795.
Abstract: This paper proposes a probabilistic modeling learning algorithm for the local search approach to the Multiple-Valued Logic (MVL) networks. The learning model (PMLS) has two phases: a local search (LS) phase, and a probabilistic modeling (PM) phase. The LS performs searches by updating the parameters of the MVL network. It is equivalent to a gradient decrease of the error measures, and leads to a local minimum of error that represents a good solution to the problem. Once the LS is trapped in local minima, the PM phase attempts to generate a new starting point for LS for further search. It is expected that the further search is guided to a promising area by the probability model. Thus, the proposed algorithm can escape from local minima and further search better results. We test the algorithm on many randomly generated MVL networks. Simulation results show that the proposed algorithm is better than the other improved local search learning methods, such as stochastic dynamic local search (SDLS) and chaotic dynamic local search (CDLS).
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E94.A.795/_p
Salinan
@ARTICLE{e94-a_2_795,
author={Shangce GAO, Qiping CAO, Masahiro ISHII, Zheng TANG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Local Search with Probabilistic Modeling for Learning Multiple-Valued Logic Networks},
year={2011},
volume={E94-A},
number={2},
pages={795-805},
abstract={This paper proposes a probabilistic modeling learning algorithm for the local search approach to the Multiple-Valued Logic (MVL) networks. The learning model (PMLS) has two phases: a local search (LS) phase, and a probabilistic modeling (PM) phase. The LS performs searches by updating the parameters of the MVL network. It is equivalent to a gradient decrease of the error measures, and leads to a local minimum of error that represents a good solution to the problem. Once the LS is trapped in local minima, the PM phase attempts to generate a new starting point for LS for further search. It is expected that the further search is guided to a promising area by the probability model. Thus, the proposed algorithm can escape from local minima and further search better results. We test the algorithm on many randomly generated MVL networks. Simulation results show that the proposed algorithm is better than the other improved local search learning methods, such as stochastic dynamic local search (SDLS) and chaotic dynamic local search (CDLS).},
keywords={},
doi={10.1587/transfun.E94.A.795},
ISSN={1745-1337},
month={February},}
Salinan
TY - JOUR
TI - Local Search with Probabilistic Modeling for Learning Multiple-Valued Logic Networks
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 795
EP - 805
AU - Shangce GAO
AU - Qiping CAO
AU - Masahiro ISHII
AU - Zheng TANG
PY - 2011
DO - 10.1587/transfun.E94.A.795
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E94-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2011
AB - This paper proposes a probabilistic modeling learning algorithm for the local search approach to the Multiple-Valued Logic (MVL) networks. The learning model (PMLS) has two phases: a local search (LS) phase, and a probabilistic modeling (PM) phase. The LS performs searches by updating the parameters of the MVL network. It is equivalent to a gradient decrease of the error measures, and leads to a local minimum of error that represents a good solution to the problem. Once the LS is trapped in local minima, the PM phase attempts to generate a new starting point for LS for further search. It is expected that the further search is guided to a promising area by the probability model. Thus, the proposed algorithm can escape from local minima and further search better results. We test the algorithm on many randomly generated MVL networks. Simulation results show that the proposed algorithm is better than the other improved local search learning methods, such as stochastic dynamic local search (SDLS) and chaotic dynamic local search (CDLS).
ER -