Abstract
One of the recent frontiers of the multiple objectives optimization strategies is the introduction of an artificial neural network in place of a more time-consuming numerical tool or approach to compute the cost functions. This paper describes the development of a multiple objectives optimization strategy for an electromagnetic bandgap common mode filter by using an artificial neural network. The network training, that is to say the evaluation of the internal weights and biases, is optimized by a genetic algorithm. The results are discussed and also compared with those stemming from an already developed multiple objective sequential optimization strategy applied to the same common mode filter. In both strategies, the cost functions are the same and the optimization algorithm is a differential evolutionary one.
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 callMore From: IEEE Transactions on Electromagnetic Compatibility
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.
