Browse > Article
http://dx.doi.org/10.5370/KIEEP.2018.67.1.047

Energy Efficiency Prediction Based on an Evolutionary Design of Incremental Granular Model  

Yeom, Chan-Uk (Dept. of Electronics Engineering, Chosun University)
Kwak, Keun-Chang (Dept. of Electronics Engineering, Chosun University)
Publication Information
The Transactions of the Korean Institute of Electrical Engineers P / v.67, no.1, 2018 , pp. 47-51 More about this Journal
Abstract
This paper is concerned with an optimization design of Incremental Granular Model(IGM) based Genetic Algorithm (GA) as an evolutionary approach. The performance of IGM has been successfully demonstrated to various examples. However, the problem of IGM is that the same number of cluster in each context is determined. Also, fuzzification factor is set as typical value. In order to solve these problems, we develop a design method for optimizing the IGM to optimize the number of cluster centers in each context and the fuzzification factor. We perform energy analysis using 12 different building shapes simulated in Ecotect. The experimental results on energy efficiency data set of building revealed that the proposed GA-based IGM showed good performance in comparison with LR and IGM.
Keywords
Incremental granular model; Genetic algorithm; Energy efficiency data; Linear regression;
Citations & Related Records
연도 인용수 순위
  • Reference
1 J. S. R. Jang, C. T. Sun, and E. Mizutani, "Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence," Prentice Hall, 1997.
2 C. J. Lin, "An efficient immune-based symbiotic particle swarm optimization learning algorithm for TSK-type neuro-fuzzy networks design," Fuzzy Sets and Systems, vol. 159, no. 21, pp. 2890-2909, 2008.   DOI
3 S. K. Oh, W. D. Kim, W. Pedrycz, and B. J. Park, "Polynomial-based radial basis function neural networks (P-RBF NNs) realized with the aid of particle swarm optimization," Fuzzy Sets and Systems, vol. 163, no. 1, pp. 54-77, 2011.   DOI
4 W. Pedrycz and A. V. Vasilakos, "Linguistic models and linguistic modeling," IEEE Trans. on Systems, Man, and Cybernetics-Part B, vol. 29, no. 6, pp. 745-757, 1999.   DOI
5 W. Pedrycz, "Conditional fuzzy c-mans," Pattern Recognition Letters, vol. 17, no. 6, pp. 625-631, 1996.   DOI
6 W. Pedrycz, "Conditional fuzzy clustering in the design of radial basis function neural networks," IEEE Trans.on Neural Networks, vol. 9, no. 4, pp. 601-612, 1998.   DOI
7 W. Pedrycz and K. C. Kwak, "Linguistic models as a framework of user-centric system modeling," IEEE Trans. on Systems, Man, and Cybernetics- Part A: Systems and Humans, vol. 36, no. 4, pp. 727-745, 2006.   DOI
8 W. Pedrycz, "A dynamic data granulation through adjustable fuzzy clustering," Pattern Recognition Letters, vol. 29, no. 16, pp. 2059-206, 2008.   DOI
9 K. C. Kwak, W. Pedrycz, "A design of genetically oriented linguistic model with the aid of fuzzy granulation," 2010 IEEE World Congress on Computational Intelligence, Barcelona, Spain, pp. 18-2, July, 2010.
10 K. C. Kwak, "A design of genetically optimized linguistic models," IEICE Trans. on Information and Systems, vol. E95D, no. 12, pp. 3117-3120, 2012.
11 W. Pedrycz, K. C. Kwak, "The development of incremental models," IEEE Trans. on Fuzzy Systems, vol. 15, no. 3, pp. 507-518, 2007.   DOI
12 Y. H. Byeon and K. C. Kwak, "A design of genetically oriented rules-based incremental granular models and its application," Symmetry, vol. 9, no. 12, Article ID 324, 2017.
13 UCI Machine Learning Repository, Available online: https://archive.ics.uci.edu/ml/datasets.