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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).

- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E94-A No.2 pp.795-805

- Publication Date
- 2011/02/01

- Publicized

- Online ISSN
- 1745-1337

- DOI
- 10.1587/transfun.E94.A.795

- Type of Manuscript
- PAPER

- Category
- Neural Networks and Bioengineering

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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

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@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},}

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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 -