Abstract
The accurate and reliable measurement of effluent quality indices is essential for the implementation of successful control and optimization of wastewater treatment plants. In order to enhance the estimate performance in terms of accuracy and reliability, we present a partial least-squares-based extreme learning machine (called PLS-ELM) in this paper. The partial least squares (PLS) regression is applied to the ELM framework to improve the algebraic property of the hidden output matrix, which can be ill-conditional due to the high multicollinearity of the hidden layer output. The main idea behind our proposed PLS-ELM is to achieve a robust generalization performance by extracting a reduced number of latent variables from the hidden layer and using orthogonal projection operations. The results from a case study of a municipal wastewater treatment plant show that the PLS-ELM can effectively capture the input–output relationship with favorable performance against the conventional ELM.
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Acknowledgments
The work is supported by NSF in China (61020106003 and 60874057), China’s Postdoctoral Science Foundation (20100471464), and also by a matching grant for 1000 talent program (P201100020).
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Zhao, L., Wang, D. & Chai, T. Estimation of effluent quality using PLS-based extreme learning machines. Neural Comput & Applic 22, 509–519 (2013). https://doi.org/10.1007/s00521-012-0837-1
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DOI: https://doi.org/10.1007/s00521-012-0837-1