Abstract
In this work, a germinal center optimization (GCO) algorithm which implements temporal leadership through modeling a non-uniform competitive-based distribution for particle selection is used to find an optimal set of parameters for a recurrent high order neural network observer (RHONNO). The RHONNO is trained with an extended Kalman filter algorithm and it is capable of giving a model of the system besides of just giving state estimation. Furthermore, the RHONNO does not need previous knowledge of the system model, nor measurements, estimation or bounds of delays and disturbances. Applicability of the proposed methodology is presented using simulation results.
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.
