Global optimization using meta-controlled Boltzmann machine

Shamshul Bahar Yaakob; Junzo Watada

doi:10.1109/ICGEC.2010.18

Global optimization using meta-controlled Boltzmann machine

Shamshul Bahar Yaakob, Junzo Watada

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this study, a new artificial neuron network model called the meta-controlled Boltzmann machine is introduced. The meta-controlled Boltzmann machine model includes the McCulloch-Pitts model, the Hopfield network, and also the Boltzmann machine. The proposed method are applied both diffusion processes and simulated annealing. The convergence proof of the proposed method is shows in this paper. Meta-controlled Boltzmann machine show an ability to solve combinatorial optimization problems better than either Hopfield networks or Boltzmann machines.

Original language	English
Title of host publication	Proceedings - 4th International Conference on Genetic and Evolutionary Computing, ICGEC 2010
Pages	39-42
Number of pages	4
DOIs	https://doi.org/10.1109/ICGEC.2010.18
Publication status	Published - 2010
Event	4th International Conference on Genetic and Evolutionary Computing, ICGEC 2010 - Shenzhen Duration: 2010 Dec 13 → 2010 Dec 15

Other

Other	4th International Conference on Genetic and Evolutionary Computing, ICGEC 2010
City	Shenzhen
Period	10/12/13 → 10/12/15

Keywords

Boltzmann machine
Hopfield networks
Meta-controlled Boltzmann machine
Neural network
Simulated annealing

ASJC Scopus subject areas

Computational Theory and Mathematics
Theoretical Computer Science

Access to Document

10.1109/ICGEC.2010.18

Cite this

@inproceedings{5f82d2e2b2484590af2e70ff7b9360c3,

title = "Global optimization using meta-controlled Boltzmann machine",

abstract = "In this study, a new artificial neuron network model called the meta-controlled Boltzmann machine is introduced. The meta-controlled Boltzmann machine model includes the McCulloch-Pitts model, the Hopfield network, and also the Boltzmann machine. The proposed method are applied both diffusion processes and simulated annealing. The convergence proof of the proposed method is shows in this paper. Meta-controlled Boltzmann machine show an ability to solve combinatorial optimization problems better than either Hopfield networks or Boltzmann machines.",

keywords = "Boltzmann machine, Hopfield networks, Meta-controlled Boltzmann machine, Neural network, Simulated annealing",

author = "Yaakob, {Shamshul Bahar} and Junzo Watada",

year = "2010",

doi = "10.1109/ICGEC.2010.18",

language = "English",

isbn = "9780769542812",

pages = "39--42",

booktitle = "Proceedings - 4th International Conference on Genetic and Evolutionary Computing, ICGEC 2010",

note = "4th International Conference on Genetic and Evolutionary Computing, ICGEC 2010 ; Conference date: 13-12-2010 Through 15-12-2010",

}

TY - GEN

T1 - Global optimization using meta-controlled Boltzmann machine

AU - Yaakob, Shamshul Bahar

AU - Watada, Junzo

PY - 2010

Y1 - 2010

N2 - In this study, a new artificial neuron network model called the meta-controlled Boltzmann machine is introduced. The meta-controlled Boltzmann machine model includes the McCulloch-Pitts model, the Hopfield network, and also the Boltzmann machine. The proposed method are applied both diffusion processes and simulated annealing. The convergence proof of the proposed method is shows in this paper. Meta-controlled Boltzmann machine show an ability to solve combinatorial optimization problems better than either Hopfield networks or Boltzmann machines.

AB - In this study, a new artificial neuron network model called the meta-controlled Boltzmann machine is introduced. The meta-controlled Boltzmann machine model includes the McCulloch-Pitts model, the Hopfield network, and also the Boltzmann machine. The proposed method are applied both diffusion processes and simulated annealing. The convergence proof of the proposed method is shows in this paper. Meta-controlled Boltzmann machine show an ability to solve combinatorial optimization problems better than either Hopfield networks or Boltzmann machines.

KW - Boltzmann machine

KW - Hopfield networks

KW - Meta-controlled Boltzmann machine

KW - Neural network

KW - Simulated annealing

UR - http://www.scopus.com/inward/record.url?scp=79952550755&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79952550755&partnerID=8YFLogxK

U2 - 10.1109/ICGEC.2010.18

DO - 10.1109/ICGEC.2010.18

M3 - Conference contribution

AN - SCOPUS:79952550755

SN - 9780769542812

SP - 39

EP - 42

BT - Proceedings - 4th International Conference on Genetic and Evolutionary Computing, ICGEC 2010

T2 - 4th International Conference on Genetic and Evolutionary Computing, ICGEC 2010

Y2 - 13 December 2010 through 15 December 2010

ER -

Global optimization using meta-controlled Boltzmann machine

Abstract

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this