Application of multilayer perceptron and radial basis function neural network in steady state modeling of automotive air conditioning system
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-i...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Conference or Workshop Item |
| Published: |
2013
|
| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/50910/ http://dx.doi.org/10.1109/ICCSCE.2012.6487219 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Universiti Teknologi Malaysia |
| Summary: | 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. |
|---|