Fuzzy model and control of a fan-coil

Ghiaus, Christian

doi:10.1016/s0378-7788(00)00097-9

Cited by 37 publications

(19 citation statements)

References 24 publications

(22 reference statements)

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“…Important HVAC engineering problems may be seen as combinations of direct and inverse modeling problems represented on psychrometric charts: analysis and solution verification [13e15], parameter optimization in design [16e19], performance evaluation [20e24], efficient control [25], fault detection and diagnosis [26], etc.…”

Section: Dynamic Models For Energy Estimation Vs Steady-state Modelsmentioning

confidence: 99%

Linear algebra solution to psychometric analysis of air-conditioning systems

Ghiaus¹

2014

Energy

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Section: Dynamic Models For Energy Estimation Vs Steady-state Modelsmentioning

confidence: 99%

Linear algebra solution to psychometric analysis of air-conditioning systems

Ghiaus¹

2014

Energy

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“…The model performance can be tested in two different ways: recursive estimation and one-step-ahead prediction [Ghiaus, 2001]. Here a recursive estimator, which predicts ˆ( 1) y k + based on previous predictions, is used to test the output of the second-order model.…”

Section: Validation Of Model Performancementioning

confidence: 99%

“…, which uses the current and previous measurements of the actual output, since most models will give satisfactory results when tested in this way [Ghiaus, 2001]. …”

Section: Validation Of Model Performancementioning

confidence: 99%

Generating training data for identifying neurofuzzy models of non-linear dynamic systems

Zhou

Dexter

Zolotas

2009

Proceedings of the 48h IEEE Conference on Decision and Control (CDC) Held Jointly With 2009 28th Chinese Control Conference

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. Generating training data for identifying neurofuzzy models of non-linear dynamic systems. IN: Joint 48th IEEE Conference on Decision and Control and 28th ChineseControl Conference, Shanghai, China, Additional Information:• This is a conference paper [ c 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. For the full text of this licence, please go to: http://creativecommons.org/licenses/by-nc-nd/2.5/ Abstract-This paper presents a methodology for generating data for training a fuzzy relational model, one neuro-fuzzy modeling technique. Neuro-fuzzy modeling is a popular "grey-box" modeling technique used to model complex, non-linear plants utilizing input-output data, i.e. as an alternative to physical-based modeling. The controllable input variables of each of the generated training data set, are positioned at the centres of the fuzzy sets, so that the steadystate and dynamic performance of the model should be satisfactory whenever the control signal is stepped between the centres of its fuzzy sets. The rule confidences of the fuzzy rules are identified via the Global Least-Square (GLS) identification algorithm. The model performance is validated by using a simulated water level control system.

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“…Jiang [13] developed an FCU fuzzy controller, which used fuzzy logic to regulate fan speed and the three-way valve bypass ratio of the FCU system. Ghiaus [14] adopted a T-S type fuzzy model to describe FCU operational performance and then presented a model-based control method for typical FCUs. A comparative work between PID control and fuzzy control on FCU control applications was conducted.…”

Section: Introductionmentioning

confidence: 99%