Physical Rules Based Adaptive Neuro-Fuzzy Inferential Sensor Model Design and Analysis in Predicting the Indoor Temperature in Heating System

Abstract:

Article Preview

The previous research on adaptive neuro-fuzzy inferential systems (ANFIS) presented an approach to estimating the average indoor temperature in the building environment. However, the restriction on robustness limited the energy efficiency and indoor comfort ratio. An accurate and robust prediction model is proposed in this paper. Comparing to the previous unphysical rules based ANFIS prediction model, the improvement of the physical rules based ANFIS prediction model will be presented and the reason of better performance of this new model will be discussed. Three performance measures are using in evaluating the proposed prediction model.

Info:

Periodical:

Advanced Materials Research (Volumes 516-517)

Edited by:

Jinyue Yan, Charles C. Zhou, Rutang Liao and Jianwen Wang

Pages:

370-379

DOI:

10.4028/www.scientific.net/AMR.516-517.370

Citation:

L. Huang et al., "Physical Rules Based Adaptive Neuro-Fuzzy Inferential Sensor Model Design and Analysis in Predicting the Indoor Temperature in Heating System", Advanced Materials Research, Vols. 516-517, pp. 370-379, 2012

Online since:

May 2012

Export:

Price:

$35.00

In order to see related information, you need to Login.

In order to see related information, you need to Login.