TY - CHAP

T1 - Structural learning model of the neural network and its application to LEDs signal retrofit

AU - Watada, Junzo

AU - Yaakob, Shamshul Bahar

PY - 2011

Y1 - 2011

N2 - The objective of this research is to realize structural learning within a Boltzmann machine (BM), which enables the effective solution of problems defined in terms of mixed integer quadratic programming. Simulation results show that computation time is up to one fifth faster than conventional BMs. The computational efficiency of the resulting double-layer BM is approximately expressed as the ratio n divided by N, where n denotes the number of selected units (neurons/nodes), and N the total number of units. The double-layer BM is applied to efficiently solve the mean-variance problem using mathematical programming with two objectives: the minimization of risk and the maximization of expected return. Finally, the effectiveness of our method is illustrated by way of a light emitting diodes (LED) signal retrofit example. The double-layer BM enables us to not only obtain a more effective selection of results, but also enhance effective decision making. The results also enable us to reduce the computational overhead, as well as to more easily understand the structure. In other words, decision makers are able to select the best solution given their respective points of view, by means of the alternative solution provided by the proposed method.

AB - The objective of this research is to realize structural learning within a Boltzmann machine (BM), which enables the effective solution of problems defined in terms of mixed integer quadratic programming. Simulation results show that computation time is up to one fifth faster than conventional BMs. The computational efficiency of the resulting double-layer BM is approximately expressed as the ratio n divided by N, where n denotes the number of selected units (neurons/nodes), and N the total number of units. The double-layer BM is applied to efficiently solve the mean-variance problem using mathematical programming with two objectives: the minimization of risk and the maximization of expected return. Finally, the effectiveness of our method is illustrated by way of a light emitting diodes (LED) signal retrofit example. The double-layer BM enables us to not only obtain a more effective selection of results, but also enhance effective decision making. The results also enable us to reduce the computational overhead, as well as to more easily understand the structure. In other words, decision makers are able to select the best solution given their respective points of view, by means of the alternative solution provided by the proposed method.

KW - Boltzmann machine (BM)

KW - double-layer BM

KW - Hopfield network

KW - mean-variance analysis

KW - quadratic programming

KW - Structural learning

UR - http://www.scopus.com/inward/record.url?scp=80054764030&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80054764030&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-11739-8_3

DO - 10.1007/978-3-642-11739-8_3

M3 - Chapter

AN - SCOPUS:80054764030

SN - 9783642117381

VL - 372

T3 - Studies in Computational Intelligence

SP - 55

EP - 74

BT - Studies in Computational Intelligence

ER -