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- W2897328429 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." @default.
- W2897328429 created "2018-10-26" @default.
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- W2897328429 date "2018-10-17" @default.
- W2897328429 modified "2023-10-08" @default.
- W2897328429 title "Model Research on CFBB’s Boiler Efficiency Based on an Improved Online Learning Neural Network" @default.
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- W2897328429 doi "https://doi.org/10.1007/978-3-030-01520-6_12" @default.
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