K-nearest neighbour technique for the effective prediction of refrigeration parameter compatible for automobile

Manufacturing simulation is an encouraging research area in resent decade. Creation or development of better simulation tool or technique is one of the major intension in manufacturing simulation. In resent research most of the manufacturing processes are simulated successfully. But some processes a...

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Vydáno v:Thermal science Ročník 24; číslo 1 Part B; s. 565 - 569
Hlavní autoři: Perundyurai Thangavel, Saravanakumar, Vellingiri, Suresh, Rajendrian, Srinivasan, Munusamy, Sundarrajan, Chinnaiyan, Saravanan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Belgrade Society of Thermal Engineers of Serbia 2020
Témata:
Air conditioning
Computer simulation
Machine learning
Manufacturing
Parameters
Refrigeration
Simulation
Support vector machines
ISSN:0354-9836, 2334-7163
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Shrnutí:Manufacturing simulation is an encouraging research area in resent decade. Creation or development of better simulation tool or technique is one of the major intension in manufacturing simulation. In resent research most of the manufacturing processes are simulated successfully. But some processes are not yet simulated effectively, especially automatic air conditioning (AC) system or refrigeration system. The automatic AC system for the passenger vehicle are not yet effectively simulated. Hence in this paper a machine learning technique is adopted for the effective prediction of parameter of automatic AC system. The proposed system uses k-nearest neighbour technique for the prediction of parameter will less error and high accuracy. The proposed system is implemented using MATLAB and its performance is compared with the support vector machine and ANN in terms of mean square error and accuracy. The proposed technique out-performs the conventional technique and suggest that the k-nearest neighbour become the most suitable technique for the modelling and performance analysis of automatic AC system.
Bibliografie:ObjectType-Article-1
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ISSN:0354-9836
2334-7163
DOI:10.2298/TSCI190623436P