Abstract
The cascade-correlation(CC) is presented as a neural network growing technique which allows one to gradually build network architecture without the need to redefine the number of neurons to be used in a feed forward. In view of the actual situation that the corresponding space curved surface which expresses pressure fluctuation in draft tube is too complex to be analyzed, considering the pressure fluctuation in draft tube, the network model is established based on CC algorithm and it is applied to hydropower station. Comparing with BP neural network, the experimental results show the prediction precision of the final model is higher and the prediction values are in better agreement with the real values.
Sign-in/Register to access full text options 
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.
