| Abstract |
The combination of a neural network (NN) and a genetic algorithm (GA) achieves the autonomousstructural design of a toroidal coil to optimize its resistance (R) and radiated magnetic field noise (B).Instead, of the finite element method, NNs are used to calculate R, inductance (L), and B. The NNs aretrained using the relationship between four structural parameters and the R, L, and B data set. Thetoroidal coil structures are optimized under a constraint condition of L while effectively using trainedNNs and a GA. As a result, R is reduced by 10\%, the power supply efficiency is improved by 1.3\%, andB is reduced by 17.6 dB. |
