An effective Parallel Integrated Neural Network System for industrial data prediction

Abstract 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 individual fitness of ADGWO, respectively, and the training of the model is completed through the optimization of ADGWO. Different from Grey Wolf Optimizer (GWO), ADGWO introduces the nonlinear cosine decreasing convergence factor, the weighted position update method and the central disturbance criterion, aiming to balance the exploitation and exploration. Applying PINN to the soil heavy metal datasets from Yinchuan of Ningxia and Wuhan, China for data prediction, the experimental results show that PINN has higher average prediction accuracy than several comparative models, especially an increase of 8.05% compared with Wavelet Neural Network, which proves that PINN can be effectively applied to industrial data prediction.