GA-BP neural network used for knock detection of aviation composite materials

2021 ◽  
Author(s):  
Guangkan Wang ◽  
Hongzheng Zeng ◽  
Dayong Zheng ◽  
Xiang Gao ◽  
Wei Cong ◽  
...  
Keyword(s):  
Neural Network ◽  
Composite Materials ◽  
Bp Neural Network ◽  
Knock Detection

Research on Homogenization of Composite Materials

2013 ◽  
Vol 663 ◽  
pp. 426-430
Author(s):  
Zhen Yu Zhou ◽  
Qi Wen Xue
Keyword(s):  
Neural Network ◽  
Composite Materials ◽  
Bp Neural Network ◽  
Pressure Vessels ◽  
Equivalent Material ◽  
Filament Wound ◽  
Filament Wound Composite ◽  
Output Information ◽  
Wound Composite ◽  
Composite Pressure Vessels

A numerical model is given to identify equivalent parameters of composite materials, using BP neural network algorithm. Taking Filament-wound composite pressure vessels as the research object, finite element models are first constructed .Getting node displacements as network training samples, the mechanical parameters as output information of network for effective training, the equivalent material parameters can be obtained. The satisfactory numerical validation is given and results show that the proposed method can identify the equivalent modulus and the equivalent Poisson’s ratio of the Filament-wound composite pressure vessels with precision. The computational efficiency is improved with BP neural network.

Performance Analysis of Bentonite-PVA Fiber Cement-Based Composites for Building Based on BP Neural Network

2020 ◽  
Vol 852 ◽  
pp. 209-219
Author(s):  
Zhe Shen
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Thermal Conductivity ◽  
Composite Materials ◽  
Influencing Factors ◽  
Bp Neural Network ◽  
Analysis Method ◽  
System Input ◽  
Prediction And Simulation ◽  
Pva Fiber

The paper will use BP neural network analysis method to study the thermal conductivity of bentonite and its influencing factors as a system. The heat conduction of bentonite was used as the output of the system, and its influencing factors were used as the system input to simulate. The corresponding simulation model was established to verify the thermal conductivity data. In addition, the analysis of the mechanical properties of the bentonite-PVA fiber cement-based composite materials for construction has not only laid a theoretical and realistic foundation for the prediction and simulation of the thermal conductivity of bentonite, but also has opened up the mechanical properties of the bentonite-PVA fiber cement-based composite materials a new path.

Application of Improved BPNN to Damage Detection of Composite Materials

2011 ◽  
Vol 467-469 ◽  
pp. 1097-1101
Author(s):  
Xiao Ma Dong
Keyword(s):  
Neural Network ◽  
Composite Materials ◽  
Damage Detection ◽  
Bp Neural Network ◽  
Dynamic Method ◽  
Damage Identification ◽  
Wavelet Series ◽  
Improved Algorithm

A dynamic method based on improved algorithm BP neural network for damage identification of composite materials was proposed. By using wavelet series, the features of signals were extracted and input to improved algorithm BP neural network for training the network and identifying the damages. Finally, the experiment results show that this proposed method can exactly identify the faults of composite materials.

Damage Identification of Composite Materials Based on PNN

2010 ◽  
Vol 29-32 ◽  
pp. 642-645
Author(s):  
Xiao Ma Dong
Keyword(s):  
Neural Network ◽  
Composite Materials ◽  
Bp Neural Network ◽  
Structural Damage ◽  
Damage Identification ◽  
Performance Comparison ◽  
Measurement Noise ◽  
Dynamic Approach ◽  
Numerical Examples ◽  
Structural Damage Identification

In recent years, there were been increasing researches focusing on the application of artificial neural networks in structural damage identification. Most of them perform well with numerical examples under error-free conditions, but become worse when the experimental data are polluted with measurement noise. In this paper, a dynamic approach based on PNN for damage identification of composite materials was proposed. By using wavelet series, the features of signals were extracted and input to PNN for training the network and identifying the damages. A performance comparison between the PNN and BPNN for structural damage identification was carried out. The results show that the proposed method can more exactly identify the faults than the BP neural network.

Research on Flaw Detection for 3-D Braided Composite Material Using Ultrasonic Testing Based on Wavelet Packet Transform

2010 ◽  
Vol 146-147 ◽  
pp. 394-399 ◽  
Author(s):  
Xiao Li He ◽  
Chong Liu
Keyword(s):  
Neural Network ◽  
Composite Material ◽  
Composite Materials ◽  
Bp Neural Network ◽  
Ultrasonic Testing ◽  
Wavelet Packet ◽  
Flaw Detection ◽  
Wavelet Packet Transform ◽  
Braided Composite ◽  
Micro Cracks

A method used to recognize the inner defects of 3-D braided composite materials is discussed. Firstly, the link between UT signals and the defects of 3-D braided composite material is analyzed. Then, the wavelet packet transform is used to process the ultrasonic scanning pulse signals of the defects. The characteristic quantities of signal are extracted into the BP neural network as samples. Through training the BP neural network, the recognize system of micro-cracks and pores is achieved. Finally, according to the results of experiment this classification system based on wavelet packet transform is proved to be feasible.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

Sign in / Sign up

Export Citation Format

Share Document