Abstract
Parameter identification of bilinear systems has been considered as an evolutionary computing algorithm-based optimization problem in this paper. A new Levy shuffled frog leaping algorithm (LSFLA), which is an improved version of the conventional shuffled frog leaping algorithm (SFLA), has been designed and has been applied for this parameter identification task. LSFLA offers enhanced local search behaviour in comparison with other traditional evolutionary computing algorithms. The ability of the new algorithm in accurately modeling parameters in single input single output (SISO) as well as multiple input multiple output (MIMO) has been checked using an extensive simulation study. The parameter estimation efficiency of the new scheme has been compared with that obtained using other popular evolutionary computing algorithms and the simulation study reveals the enhanced parameter identification ability of the proposed LSFLA.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: 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.