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Indoor Air Quality Assessment Based on Genetic Artificial Neural Network

Du, Zhe-Hua

A new method for indoor air quality assessment with training artificial neural network prediction model was presented. Genetic algorithm was used to train artificial neural network prediction model in this method. The train sample for artificial neural network learning model was grading standard. According to the weight and threshold after network study, this method could be used to make assessment on indoor air quality. The example of indoor air quality assessment demonstrated that this method improved prediction precision. This assessment method is more accurate than the traditional assessment methods. Moreover, it has the ability of global optimization, self-learning, self-adaption. This algorithm overcomes the shortcomings of traditional BP artificial neural network algorithm.

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Indoor Air Quality Assessment Based on Genetic Artificial Neural Network

Du, Zhe-Hua

A new method for indoor air quality assessment with training artificial neural network prediction model was presented. Genetic algorithm was used to train artificial neural network prediction model in this method. The train sample for artificial neural network learning model was grading standard. According to the weight and threshold after network study, this method could be used to make assessment on indoor air quality. The example of indoor air quality assessment demonstrated that this method improved prediction precision. This assessment method is more accurate than the traditional assessment methods. Moreover, it has the ability of global optimization, self-learning, self-adaption. This algorithm overcomes the shortcomings of traditional BP artificial neural network algorithm.

Indoor Air Quality Assessment Based on Genetic Artificial Neural Network

Du, Zhe-Hua

A new method for indoor air quality assessment with training artificial neural network prediction model was presented. Genetic algorithm was used to train artificial neural network prediction model in this method. The train sample for artificial neural network learning model was grading standard. According to the weight and threshold after network study, this method could be used to make assessment on indoor air quality. The example of indoor air quality assessment demonstrated that this method improved prediction precision. This assessment method is more accurate than the traditional assessment methods. Moreover, it has the ability of global optimization, self-learning, self-adaption. This algorithm overcomes the shortcomings of traditional BP artificial neural network algorithm.

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Title:

Indoor Air Quality Assessment Based on Genetic Artificial Neural Network

Contributors:

Du, Zhe-Hua (author)

Published in:

ICEEP, International Conference on Energy and Environmental Protection, 2013 ; 1147-1150

Advanced Materials Research ; 726-731

Publication date:

2013

Size:

4 Seiten

ISSN:

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.726-731.1147

Type of media:

Conference paper

Type of material:

Print

Language:

English

Keywords:

Bewertung , Raumluft , künstliches neuronales Netz , Raumluftqualität , genetischer Algorithmus , Prädiktion , Algorithmus

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