The Research on Improving the Precision of The Polymer IFHI By BP Neural Network of The Method Data Normalization. Issue 3 (March 2021)

• Record Type: Journal Article
• Authors: Zhang, Dingran; Chen, Lei; Wang, Yiqi; Wang, Huiya
• Is Part Of: IOP conference series. Volume 692:Issue 3(2021)
• Publication Date: 2021-03
• Subjects: Polymer materials -- data normalization -- BP neural network -- IFHI; Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/692/3/032067
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: BP neural network has a good effect on data prediction. Using BP neural network to predict polymer fire risk index (IFHI) is a cross research method based on computer intelligent learning model and traditional fire risk assessment. In order to solve the defect of low precision of BP neural network in IFHI prediction, a solution to standardize the initial data processing is proposed to optimize the BP neural network simulation model. In the construction of BP neural network model, the range, threshold, and weight of the initial data need to be processed globally, and then the BP neural network is simulated and trained. Finally, the data is simulated and predicted by using MATLAB. The output results of the neural network with different data standardization processing methods are compared to verify the simulation accuracy. The results show that the input node parameters of BP neural network are processed by Z-score normalization method, which improves the learning ability of the model, improves the accuracy of the prediction effect, and reduces the IFHI error of prediction.