A Recurrent RBF Neural Network Based on Modified Gravitational Search Algorithm

摘要

Recently, Gravitational search algorithm (GSA) was considered as one method for optimizing functions and solving real problems. For the sake of better adjust the values of recurrent RBF neural network (RRBFNN) to make the network achieve better performance, the MGSA is essential in this article. The advised work achieves a better compromise between exploration and development. At the same time, by increasing the guidance of the global optimal particle, the problem that the gravitational search algorithm converges slowly in the later iteration is solved. The Experiment found that the network has better convergence speed and better test accuracy than the RRBFN optimized by the conventional optimization algorithm.