Abstract
Aiming at the accuracy prediction of combustion efficiency for a 300 MW circulating fluidized bed boiler (CFBB), a online sequential circular convolution parallel extreme learning machine (OCCPELM) is proposed and applied to build the models of boiler efficiency. In OCCPELM, the circular convolution theory is introduced to map the hidden layer information into higher-dimension information; in addition, the input layer information is directly transmitted to its output layer, which makes the whole network into a double parallel construction. Four UCI regression problems and Mackey-Glass chaotic time series are employed to verify the effectiveness of OCCPELM. Finally, this paper establishes a model of combustion efficiency for a CFBB. Some comparative simulation results show that OCCPELM with less hidden units owns better generalization performance and repeatability.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China [Grant No. 61403331, 61573306], the Natural Science Foundation of Hebei Province [Grant No. F2016203427], the China Postdoctoral Science Foundation [Grant No. 2015M571280], the Doctorial Foundation of Yanshan Univerity [Grant No. B847], the natural science foundation for young scientist of Hebei province [Grant No. F2014203099] and the independent research program for young teachers of yanshan university [Grant No. 13LG006].
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Li, G., Chen, B., Qi, X., Zhang, L. (2019). Model Research on CFBB’s Boiler Efficiency Based on an Improved Online Learning Neural Network. In: Cao, J., Vong, C., Miche, Y., Lendasse, A. (eds) Proceedings of ELM-2017. ELM 2017. Proceedings in Adaptation, Learning and Optimization, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-030-01520-6_12
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DOI: https://doi.org/10.1007/978-3-030-01520-6_12
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