Abstract
Based on the nonlinearity in direct torque control (DTC) system, a modified PSO (particle swarm optimization) algorithm is proposed to optimize BP (back-propagation) neural network and structure the rotational speed identifier. Combined a linear digression method of inertia weight with a particle turning laws, this algorithm can accelerate the convergence speed of BP neural network and realize global search. Compared with results of three modified BP neural network, simulations show that the modified PSO-BP neural network can make the system to have better static and dynamic performance.
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.
