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A methodology for modeling HVAC components using evolving fuzzy rules

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conference contribution
posted on 2012-07-27, 11:15 authored by Plamen Angelov, Richard BuswellRichard Buswell, Victor I. Hanby, Jonathan WrightJonathan Wright
A methodology for the evolutionary construction of fuzzy rule-based (FRB) models is proposed in the paper. The resulting models are transparent and existing expert knowledge could easily be incorporated into the model (both at initialisation stages and during its generation). An additional advantage of the model is represented by the economy in computational effort in generating the model output. A new encoding mechanism is used that allows the fuzzy model rule base structure and parameters to be estimated from training data without establishing the complete rule list. It uses rule indices and therefore significantly reduces the computational load. The rules are extracted from the data without using a priori information about the inherent model structure. It makes FRB models as flexible as other types of 'black-box' models (neural networks, polynomial models etc.) and in the same time significantly more transparent, especially when only small subset of all possible rules is considered. This approach is applied to modelling of components of heating ventilating and air-conditioning (HVAC) systems. The FRB models have potential applications in simulation, control and fault detection and diagnosis.

History

School

  • Architecture, Building and Civil Engineering

Citation

ANGELOV, P.P. ... et al., 2000. A methodology for modeling HVAC components using evolving fuzzy rules. IN: 26th Annual Conference of the IEEE Industrial Electronics Society, Nagoya, Japan, IECON 2000, 1 (1), pp. 247 - 252

Publisher

© IEEE

Version

  • AM (Accepted Manuscript)

Publication date

2000

Notes

This is a conference paper from the 26th Annual Conference of the IEEE Industrial Electronics Society (IECON) [© IEEE]. It is also available at: http://ieeexplore.ieee.org/. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

ISBN

0780364562

ISSN

1553-572X

Language

  • en

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