Abstract

In this paper, a complex multi-layer neural network that is conducted its training by using complex back propagation algorithm is applied to solve inverse kinematics a robot manipulator and to control an inverted pendulum. Computational experiments are carried out to investigate the characteristics of the complex back propagation algorithm. Experimental results of the inverse kinematics show that the performance of complex neural network is a little advantage than a real number neural network in local minimum problem. Experimental results of the inverted pendulum also show that the complex neural network learns faster than the real number neural network and achieves higher success rate in generalization problems.

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 call

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.