Abstract:
Aiming at the poor generalization ability of single neural networks and large fluctuations of test accuracy for different samples,this paper presents an integrated neural network method based on the just-in-time learning.Firstly,several different neural network models are established based on the training samples.Secondly,several adjacent samples closest to the predicted samples are selected based on the just-in-time learning while predicting the samples.According to the training errors of the sub-networks on the adjacent samples,the integrated weights of the neural networks are generated immediately to establish the integrated neural network model in real time for predicting the test samples.Finally,the proposed method is applied to predict the naphtha dry point and a better prediction result is achieved,compared with the existing methods.