Selective combination in multiple neural networks prediction using independent component regression approach

Authors

  • See Lee Foon School of Chemical Engineering,Engineering Campus, Universiti Sains Malaysia, Seri Ampangan, 14300 Nibong Tebal, Seberang Perai Selatan, Pulau Pinang
  • Nazira Anisa Rahim River Basin Research Centre, National Hydraulic Research Institute of Malaysia, Lot 5377, Jalan Putra Permai, 43300 Seri Kembangan, Selangor
  • Ahmad Zainal School of Chemical Engineering,Engineering Campus, Universiti Sains Malaysia, Seri Ampangan, 14300 Nibong Tebal, Seberang Perai Selatan, Pulau Pinang
  • Zhang Jie School of Chemical Engineering and Advanced Materials, University of Newcastle, Newcastle upon Tyne NE1 7RU,

DOI:

https://doi.org/10.3329/cerb.v19i0.33772

Keywords:

Independent component regression, Multiple neural network, Principle component regression, Reactive biological process

Abstract

Biological processes are highly nonlinear in nature and difficult to represent accurately by simple mathematical models. However, this problem can be solved by using neural network. Neural network is a prominent modeling tool especially when it comes to intricate process such as biological process. In this paper, a multiple single hidden layer with ten hidden neurons Feedforward Artificial Neural Network (FANN) was used to model the complex and dynamic relationships between the input (dilution rate, D) and outputs (conversion, y and dimensionless temperature value, ?) for the reactive biological process. Levenberg-Marquardt Backpropagation training method was used. The multiple neural networks predicted outputs were then combined through three different methods which area simple averaging, Principal Component Regression (PCR) and Independent Component Regression (ICR). Multiple neural networks which were created by the bootstrap approach help improved single neural network performance as well as the model robustness for nonlinear process modeling. Comparison was made between the three methods. The result showed that ICR is slightly superior between the three methods especially in noise level 1,2 and 3, however ICR slightly suffer in noise level 4 and 5. This is due to the independent component regression used the latent factors and non-Gaussian distribution of y and ? values for the combination.

Chemical Engineering Research Bulletin 19(2017) 12-19

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Author Biography

See Lee Foon, School of Chemical Engineering,Engineering Campus, Universiti Sains Malaysia, Seri Ampangan, 14300 Nibong Tebal, Seberang Perai Selatan, Pulau Pinang



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Published

2017-09-10

How to Cite

Foon, S. L., Rahim, N. A., Zainal, A., & Jie, Z. (2017). Selective combination in multiple neural networks prediction using independent component regression approach. Chemical Engineering Research Bulletin, 19, 12–19. https://doi.org/10.3329/cerb.v19i0.33772

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Articles