Beetle Antennae Search Strategy for Neural Network Model Optimization with Application to Glomerular Filtration Rate Estimation

摘要

The determination of weights is very important for neural network models. Nevertheless, the traditional feedforward neural networks usually use the method of random initial values to determine the weights of the neural networks, such a method would make the related neural network models possess unstable performance in accuracy. Therefore, in order to improve the prediction accuracy and efficiency of neural networks, a novel neural network model based on beetle antennae search (BAS) and weights and structure policy (WASP) is proposed and applied to the estimation of glomerular filtration rate (GFR). Through a series of numerical experiments, it is proved that the accuracy of the proposed neural network model for GFR estimation is higher than that of the model without BAS algorithm optimization. In addition, compared with the traditional one-step method, the method proposed in this article is more than 300% higher in the three performance indicators of MSE, MAE, and MAPE.