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International Conference on Neural Information Processing

ICONIP 2018: Neural Information Processing pp 184–195Cite as

Prediction Based on Online Extreme Learning Machine in WWTP Application

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11305))

Abstract

Predicting the plant process performance is essential for controlling in wastewater treatment plant (WWTP), which is a complex nonlinear time-variant system. Extreme learning machine (ELM) is a single-hidden layer feed-forward neural network (SLFN), which randomly generates the feed-forward parameters without tuning the parameters from the input to the output layer. The output weights are calculated via the theory of Moore-Penrose generalized inverse and the minimum norm least-squares. In this paper, online extreme learning machine (Online ELM) is proposed as a predictor in WWTP, which trains the output weights and predicts the next outputs according to the real-time data collected from the process in an online manner. Furthermore, extensive comparison studies have been conducted by using other four neural network structures, including extreme learning machine, ELM with kernel, online sequential ELM (OSELM) and back propagation (BP) neural network.

Q. Yang—This work is supported by the National Natural Science Foundation of China (61673347, U1609214, 61751205).

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Authors and Affiliations

  1. College of Control Science and Engineering, Zhejiang University, Zheda Rd. 38, Hangzhou, 310027, China

    Weiwei Cao & Qinmin Yang

Authors
  1. Weiwei Cao
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  2. Qinmin Yang
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Corresponding author

Correspondence to Qinmin Yang .

Editor information

Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi Sing Leung

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

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Cao, W., Yang, Q. (2018). Prediction Based on Online Extreme Learning Machine in WWTP Application. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11305. Springer, Cham. https://doi.org/10.1007/978-3-030-04221-9_17

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  • DOI: https://doi.org/10.1007/978-3-030-04221-9_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04220-2

  • Online ISBN: 978-3-030-04221-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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