Abstract
In this paper, Cascade Forward Back Propagation (CFBP) based on neural network is proposed for the estimation of lateral and directional derivatives of an aircraft. The proposed technique is applied on the simulated flight data of research aircraft ‘HANSA’ to extract derivatives of lateral and directional motion. The results obtained using the proposed technique are compared with results obtained using existing Feed Forward Neural Network (FFNN) and Elman Network Technique (ENT) The derivatives are also estimated using the simulated data with 5 percent and 10 percent noise to check the reliability of the proposed technique and working in real flight conditions. It is observed that Cascade Forward Back Propagation (CFBP) has great capabilities to estimate derivatives of an aircraft.
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.
