Comfort Fusion Evaluation of the Indoor Thermal Environment Based on KPCA and Genetic Neural Network

Lian Zhang; Xiao Qian Hu; Shan Li

doi:10.4028/www.scientific.net/AMM.448-453.204

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 448-453Comfort Fusion Evaluation of the Indoor Thermal...

Comfort Fusion Evaluation of the Indoor Thermal Environment Based on KPCA and Genetic Neural Network

Abstract:

For the problem of complicated nonlinear relationships among the parameters of heat comfort index PMV, KPCA (Kernel Principal Component Analysis) is used to do the feature extraction. On the basis, KPCA+BP and KPCA+GNN are utilized to forecast the heat comfort level. Simulation results show that KPCA can extract the nonlinear uncorrelated sample data, and KPCA+GNN are evaluated best with high accuracy.

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Periodical:

Applied Mechanics and Materials (Volumes 448-453)

Pages:

204-208

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.448-453.204

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Online since:

October 2013

Authors:

Lian Zhang*, Xiao Qian Hu, Shan Li

Keywords:

GNN, KPCA, Nonlinear Relationships

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* - Corresponding Author

References

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