Abstract
In this paper, because the robot is easy to fall during the imitation process, a novel Radical Basis Function (RBF) neural network algorithm based on heuristic simulated annealing adaptive particle swarm optimization Particle Swarm Optimization (PSO) algorithm is proposed to judge the mimic posture of the robot. In order to solve the problem of poor convergence speed and low accuracy of traditional RBF neural network, the PSO algorithm is used to optimize it. At the same time, in order to solve the problem that the classical PSO algorithm is easy to fall into the local optimal value, heuristic simulated annealing adaptive PSO algorithm is proposed. Experiment shows that the proposed algorithm has higher convergence speed and accuracy than BP neural network, Support Vector Machines (SVM) and traditional RBF neural network.
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.
