Structural Learning of Boltzmann Machine and its Application

Abstract:

This study proposed a way to solve problem efficiently which is through structural learning of Boltzmann machine. This method used mixed integer quadratic programming to solve the problem. An analysis is conducted by using the ideas of the reliability and risks of units assessed using a variance-covariance matrix and the effect and expanses of replacement are determined. In this study, the mean-variance analysis is formulated as a mathematical program with two objectives: (1) minimization of risk and (2) maximization of expected return. Lastly, the effectiveness of proposed method is illustrated by way of a life cycle management example. The result of this suggested method was demonstrated at the end. By using this method, more effective selection of results is gathered. Thus, this prove that the effectiveness of the decision making process can be reinforced.

• Periodical: Applied Mechanics and Materials (Volume 785)
• Pages: 63-67
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.785.63
• Online since: August 2015
• Authors: Shamshul Bahar Yaakob*, Mohd Zamri Hasan, Amran Ahmed
• Keywords: Boltzmann Machine, Hopfield Neural Network, Mean-Variance Analysis, Portfolio Theory, Structural Learning

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