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Application of multiplayer perceptron and radial basis funtion neural network IN steady state modeling of automotive air conditioning system

Boon, Chiang Ng and Mat Darus, Intan Zaurah and Mohamed Kamar, Haslinda and Norazlan, Mohamed (2012) Application of multiplayer perceptron and radial basis funtion neural network IN steady state modeling of automotive air conditioning system. In: 2012 IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2012), 23-25 Nov 2012, Penang, Malaysia.

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Official URL: http://ieeexplore.ieee.org/document/6487219/

Abstract

In this paper, steady-state models of an automotive air conditioning (ACC) are identified based on two different artificial neural networks (ANN) architectures: Multilayer Perceptron Neural Networks (MLPNN) and Radial Basis Function Neural Networks (RBFNN). The ANN models are developed with a four-in three-out configuration to simulate the outlet evaporating air temperature, cooling capacity, and compressor power under different combination of input compressor speeds, evaporating air speeds, air temperature upstream of the condenser and evaporator. The required data for the system identification are collected from an experimental bench made up of the original components of an AAC system. Investigations signify the advantage of a RBFNN model over MLPNN in modeling the AAC system.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Radial basis function networks, air conditioning, automotive components
Subjects:T Technology > TJ Mechanical engineering and machinery
Divisions:Mechanical Engineering
ID Code:34003
Deposited By: Liza Porijo
Deposited On:09 Aug 2017 08:38
Last Modified:28 Sep 2017 07:03

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