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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
