At present, General Regression Neural Network (GRNN) have been more and more used for data prediction in industry, however, because its smoothing factor is difficult to determine, it is easy to obtain poor prediction accuracy when using it to predict complex problems in reality. To tackle these problems, an effective Parallel Integrated Neural Network System (PINN) is proposed in this paper. The model is a combination of GRNN and Adaptive Dynamic Grey Wolf Optimizer (ADGWO), in this model, the smoothing factor and calculation result of GRNN are taken as the individual position information and ...
