Key Variables Soft Measurement of Wastewater Treatment Process Based on Hierarchical Extreme Learning Machine
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Publisher
Springer International Publishing
Link
http://link.springer.com/content/pdf/10.1007/978-3-030-23307-5_6
Reference18 articles.
1. Huang, M., Ma, Y., Wan, J., Chen, X.: A sensor-software based on a genetic algorithm-based neural fuzzy system for modeling and simulating a wastewater treatment process. Appl. Soft Comput. 27, 1–10 (2015). https://doi.org/10.1016/j.asoc.2014.10.034
2. Trends in extreme learning machines: a review. Neural Netw. 61, 32–48 (2015). https://doi.org/10.1016/j.neunet.2014.10.001
3. Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No. 04CH37541). vol. 2, pp. 985–990, July 2004. https://doi.org/10.1109/IJCNN.2004.1380068
4. Tang, J., Deng, C., Huang, G.: Extreme learning machine for multilayer perceptron. IEEE Trans. Neural Netw. Learn. Syst. 27(4), 809–821 (2016). https://doi.org/10.1109/TNNLS.2015.2424995
5. Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1), 489–501 (2006). https://doi.org/10.1016/j.neucom.2005.12.126 . Neural Networks
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