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A Study on Optimization Approach for the Quantification Analysis Problem Using Neural Networks  

Lee, Dong-Myung (동명정보대학 컴퓨터공학과)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.10, no.1, 2006 , pp. 206-211 More about this Journal
Abstract
The quantification analysis problem is that how the m entities that have n characteristics can be linked to p-dimension space to reflect the similarity of each entity In this paper, the optimization approach for the quantification analysis problem using neural networks is suggested, and the performance is analyzed The computation of average variation volume by mean field theory that is analytical approximated mobility of a molecule system and the annealed mean field neural network approach are applied in this paper for solving the quantification analysis problem. As a result, the suggested approach by a mean field annealing neural network can obtain more optimal solution than the eigen value analysis approach in processing costs.
Keywords
평균장 신경회로망;시뮬레이티드 어닐링;수량화 문제;고유치 분석;
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1 Randall S. Sexton, 'Optimization of Neural Networks : A comparative Analysis of de Genetic Algorithm and Simulated Annealing,' European Journal of Operational Research, Vol. 114, No. 3, pp.589-601, 1999   DOI   ScienceOn
2 Yao X., 'A new simulated annealing algorithm,' International Journal of Computer Mathematics, 56, pp.161-168, 1995   DOI   ScienceOn
3 J. Hopfield, D. Tank, 'Neural' Computation of Decisions in Optimization Problems,' Bio. Cybern., 52, pp.141-152, 1985
4 Rojas F., Alvarez M. R., Salmeron M., Puntonet C. G. and Martin-Clemente R, 'Adaptive and heuristic approaches for nonlinear source separation,' Proceedings of the 2003 International Joint Conference on Neural Networks, Vol. 1, pp.720-725, 2003
5 Peterson C. and Hartman E., 'Explorations of the Mean Field Theory Learning Algorithm,' Neural Networks 2, pp.475-494, 1989   DOI   ScienceOn
6 Van den Bout and Miller III, 'Graph Partitioning Using Annealed Neural Net-works,' IEEE Transactions on Neural Networks, 1(2), pp.192-203, 1990   DOI
7 Lei Cheng and Xiuwen Liu, 'Sparse linear representations for recognition,' Proceedings of the 2003 International Joint Conference on Neural Networks, Vol. 2, pp.1324-1327, 2003
8 Rose J., Klebsch W. and Wolf J., 'Temperature measurement and equilibrium dynamics of simulated annealing placements,' Computer Aided Design of Integrated Circuits and Systems, IEEE Transactions on Vol. 9, Issue 3, pp.253-259, 1990   DOI   ScienceOn
9 Kirkpatrick S., Gelatt. C. and Vecchi M., 'Optimization by Simulated Annealing,' Science 220, pp. 671-680, 1983   DOI   ScienceOn
10 Hamm. L., Wade Brorsen and B. Hagan, 'Global optimization of neural network weights,' Proceedings of the 2002 International Joint Conference on Neural Networks, Vol. 2, pp.1228-1233, 2002
11 Liang, R. H. and Kang F. C., 'Thermal generating unit commitment using an extended mean field annealing neural network,' IEE Proceedings, Vol. 147, Issue 3, pp.164-170, 2000
12 Yamazaki. A., de Souto., M. C. P. and Ludermir. T. B., 'Optimization of neural network weights and architectures for odor recognition using simulated annealing,' Proceedings of the 2002 International Joint Conference on Neural Networks, Vol. 1, pp.547-552, 2002