Structural Learning of Boltzmann Machine: An Application

Shamshul Bahar Yaakob; Zamri Hassan; Syed Akhmal Syed Jamalil

doi:10.4028/www.scientific.net/AMM.793.657

Paper Titles

Most Reliable GPS Tracking Solution for Electric Powered Sustainable Transportation Systems
p.620

Feasibility Study of Arduino Microcontroller Usage for IPMC Actuator Control
p.625

Optimization of Lift Generation for a Spinning Surface through its Design and Speed
p.630

A Management Guide for Determination of Optimal Buffer Allocation in Unpaced Production Line Using 6 Steps OBA
p.637

Feature Extraction and Optimum Part Deposition Orientation for FDM
p.642

The Relationship between Managerial Commitment towards Manufacturing Capabilities: Investigating in an Electrical and Electronic Industries in Malaysia
p.647

The Influence of Strategic Alignment on Company Performance in Malaysian Electrical and Electronic Industries
p.652

Structural Learning of Boltzmann Machine: An Application
p.657

The Relationship of Training on Manufacturing Capabilities among Electrical and Electronic Manufacturers in Malaysia
p.663

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 793Structural Learning of Boltzmann Machine: An...

Structural Learning of Boltzmann Machine: An Application

Abstract:

In order to solve a problem efficiently, we propose the structural learning of Boltzmann machine. The proposed method enables us to solve the problem defined in terms of mixed integer quadratic programming. In this research, an analysis is performed by using the concepts of the reliability and risks of units evaluated using a variance-covariance matrix and also the effect and expanses of replacement are measured. Mean-variance analysis is formulated as a mathematical programming with two objectives to minimize the risk and maximize the expected return. Finally, we employ a Boltzmann machine to solve the mean-variance analysis efficiently. At the end, the result of our method was exemplified. This method enables us to obtain a more effective selection of results and enhanced the effectiveness of the decision making process.

You might also be interested in these eBooks

Electrical Power Engineering and Sustainable Development of Industry

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 793)

Pages:

657-662

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.793.657

Citation:

Cite this paper

Online since:

September 2015

Authors:

Shamshul Bahar Yaakob*, Zamri Hassan, Syed Akhmal Syed Jamalil

Keywords:

Boltzman, Machine, Mean-Variance

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] H. Markowitz, Mean-Variance Analysis in Portfolio Choice and Capital Markets, Blackell, (1987).

Google Scholar

[2] D. H Ackley, G. E. Hinton, and T. J. Sejnowski A Learning Algorithm for Boltzmann Machines, Cognitive Sci., 9, (1985).

Google Scholar

[3] S. B. Yaakob, J. Watada, and J. Fulcher, Structural Learning of the Boltzmann Machine and its Application to Life Cycle Management, Neurocomputing, Vol. 74, No. 12-13, pp.2193-2200, (2011).

DOI: 10.1016/j.neucom.2011.02.018

Google Scholar

[4] S. B. Yaakob and J. Watada, Reliability Enhancement of Power Systems through a Mean-Variance Approach, Neural Computing and Applications, Vol. 21, No. 6, pp.1363-1373, (2012).

DOI: 10.1007/s00521-011-0580-z

Google Scholar

[5] S. B. Yaakob and J. Watada, Structural Learning Model of the Neural Network and its Application to LEDs Signal Retrofit, Studies in Computational Intelligence (Springer- Verlag), Vol. 372, pp.55-74, (2011).

DOI: 10.1007/978-3-642-11739-8_3

Google Scholar