| Resumen |
The optimized radial basis function network is a kind of neural network that utilizes a step size inside of the gradient strategy for the modeling, where a small step size will spend much time to reach a minimum, while a big step size will jump over the minimum; hence, it needs an acceptable step size. The genetic optimizer is one option to seek an acceptable step size. In this study, the genetic optimizer is suggested to seek an acceptable step size in the gradient strategy for an optimized radial basis function network. The difference between the other genetic optimizers and our genetic optimizer is that the other genetic optimizers utilize high number of stages, while our genetic optimizer utilizes small number of stages. The idea of utilizing small number of stages in our genetic optimizer is based on the simplex optimizer and bat optimizer which also utilize small number of stages. To validate the performance of the optimized radial basis function network, the fatigue driving modeling in a vehicle is evaluated. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. |
