scholarly journals Influence of hinge length and distribution number on the camber and cornering properties of a non-pneumatic tire

2020 ◽  
Vol 12 (12) ◽  
pp. 168781402098468
Author(s):  
Xianbin Du ◽  
Youqun Zhao ◽  
Yijiang Ma ◽  
Hongxun Fu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Adaptive Learning ◽  
High Speed ◽  
Back Propagation ◽  
Lateral Force ◽  
Learning Ability ◽  
Vehicle Performance ◽  
Neural Network Theory ◽  
Bp Neural Network Model

The camber and cornering properties of the tire directly affect the handling stability of vehicles, especially in emergencies such as high-speed cornering and obstacle avoidance. The structural and load-bearing mode of non-pneumatic mechanical elastic (ME) wheel determine that the mechanical properties of ME wheel will change when different combinations of hinge length and distribution number are adopted. The camber and cornering properties of ME wheel with different hinge lengths and distributions were studied by combining finite element method (FEM) with neural network theory. A ME wheel back propagation (BP) neural network model was established, and the additional momentum method and adaptive learning rate method were utilized to improve BP algorithm. The learning ability and generalization ability of the network model were verified by comparing the output values with the actual input values. The camber and cornering properties of ME wheel were analyzed when the hinge length and distribution changed. The results showed the variation of lateral force and aligning torque of different wheel structures under the combined conditions, and also provided guidance for the matching of wheel and vehicle performance.

Modelling of viscosity of fluorine-free mold fluxes using neural network

2019 ◽  
Vol 116 (2) ◽  
pp. 201
Author(s):  
Xiaoli Yuan ◽  
Lin Wang ◽  
Jianqiang Zhang ◽  
Oleg Ostrovski ◽  
Chen Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Viscosity Data ◽  
The Neural Network ◽  
Mold Fluxes ◽  
Experimental Values ◽  
Bp Neural Network Model

Viscosity is an important property of mold fluxes for steel continuous casting. However, direct measurement of viscosity of multi-component systems in a broad range of temperatures and compositions is an onerous work and has some limitations. This paper developed a model using the back propagation (BP) neural network to describe the viscosity of fluorine-free mold fluxes. The BP neural network model was developed and validated using 70 experimental values of viscosity of fluorine-free mold fluxes CaO-SiO2-Al2O3-B2O3-Na2O-TiO2-MgO-Li2O-MnO-ZrO2; 51 of them were used for developing the neural network model and the rest 19 viscosity data for the model validation. Calculated viscosities were in a good agreement with the experimental data. Based on the developed model, the effects of temperature and composition on the viscosity of fluorine-free fluxes were predicted and discussed.

scholarly journals Genetic Algorithm – Back Propagation (GA-BP) Neural Network for Chlorophyll-a Concentration Inversion Using Landsat 8 OLI Data

2020 ◽  
Vol 143 ◽  
pp. 02002
Author(s):  
Qi Chen ◽  
Mutao Huang ◽  
Ronghui Wang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Chl A ◽  
Bp Neural Network Model

Chlorophyll-a (Chl-a) accurate inversion in inland water is important for water environmental protection. In this study, we tested the Genetic Algorithm optimized Back Propagation (GA-BP) neural network model to precisely simulated the Chl-a in an inland lake using Landsat 8 OLI images. The result show that the R2 of GA-BP neural network model has increased 28.17% compared to traditional BP neural network model. Then this GA-BP model was applied to another two scenes of Landsat 8 OLI image with the R2 of 0.961, 0.954 respectively for March 26 2018, October 26 2018. And the spatial distribution have shown a reasonable result of Chl-a variation in Lake Donghu. This study can provide a new method for Chla concentration inversion in urban lakes and support water environment protection on a large scale.

Prediction of the Forest Health Based on BP Neural Networks

2013 ◽  
Vol 726-731 ◽  
pp. 4303-4306 ◽  
Author(s):  
Yong Wang ◽  
Zhuang Xiong
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Forest Health ◽  
Bp Neural Networks ◽  
Forest Disease ◽  
Bp Neural Network Model

This paper simple introduced back propagation (BP) neural networks, and constructed a dynamic predict model, based on it to predict forest disease and insect and rat pest. Then it analyzed and simulated with the BP neural network model with the data produced in the recent ten years. The result indicated that the BP neural network model is reliable for predicting the forest disease and insect and rat pest. The method provides scientific foundation for the forestry management of studied area.

scholarly journals Clinicopathological features of fibrosarcomatous dermatofibrosarcoma protuberans and the construction of a back-propagation neural network recognition model

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Yanan Li ◽  
Jiaqi Liang ◽  
Xuewen Xu ◽  
Xian Jiang ◽  
Chuan Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Classification Accuracy ◽  
Back Propagation ◽  
Dermatofibrosarcoma Protuberans ◽  
Clinicopathological Features ◽  
Recognition Model ◽  
Bp Neural Network Model

Abstract Background Fibrosarcomatous dermatofibrosarcoma protuberans (FS-DFSP) is a form of tumor progression of dermatofibrosarcoma protuberans (DFSP) with an increased risk of metastasis and recurrence. Few studies have compared the clinicopathological features of FS-DFSP and conventional DFSP (C-DFSP). Objectives To better understand the epidemiological and clinicopathological characteristics of FS-DFSP. Methods We conducted a cohort study of 221 patients diagnosed with DFSP and built a recognition model with a back-propagation (BP) neural network for FS-DFSP. Results Twenty-six patients with FS-DFSP and 195 patients with C-DFSP were included. There were no differences between FS-DFSP and C-DFSP regarding age at presentation, age at diagnosis, sex, size at diagnosis, size at presentation, and tumor growth. The negative ratio of CD34 in FS-DFSP (11.5%) was significantly lower than that in C-DFSP (5.1%) (P = 0.005). The average Ki-67 index of FS-DFSP (18.1%) cases was significantly higher than that of C-DFSP (8.1%) cases (P < 0.001). The classification accuracy of the BP neural network model training samples was 100%. The correct rates of classification and misdiagnosis were 84.1% and 15.9%. Conclusions The clinical manifestations of FS-DFSP and C-DFSP are similar but have large differences in immunohistochemistry. The classification accuracy and feasibility of the BP neural network model are high in FS-DFSP.

An Online Modeling Method for Real-Time Thermal Error Compensation on High-Speed Machines Based on RBF Neural Network Theory

2010 ◽  
Vol 455 ◽  
pp. 606-611 ◽  
Author(s):  
Hong Tao Zhang ◽  
Hui Jiang ◽  
Jian Guo Yang
Keyword(s):  
Neural Network ◽  
High Speed ◽  
Rbf Neural Network ◽  
Thermal Characteristics ◽  
Thermal Error ◽  
Modeling Method ◽  
Learning Ability ◽  
Online Modeling ◽  
Set Up ◽  
Neural Network Theory

This paper studies the modeling method based on RBF (Radial-Basis Function) neural network according to its learning ability, and a new neural network online model has been set up. The comparison and analysis result of the case studies shows that, when changing the working condition, the compensation effect of online modeling method is better than offline modeling method and the online model can better reflect the thermal characteristics of High-speed machine tool.

BP Neural Network Based Optimization for China Railway Freight Transport Network

2014 ◽  
Vol 1037 ◽  
pp. 404-410 ◽  
Author(s):  
Yan Sun ◽  
Mao Xiang Lang ◽  
Dan Zhu Wang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Adaptive Learning ◽  
Bp Neural Network ◽  
Freight Transport ◽  
Transport Network ◽  
Railway Freight ◽  
Bp Neural Network Model ◽  
Railway Freight Transport

In order to optimize the railway freight transport network, integrate the limited transport resources and overcome the current problems existing in the traditional transport organization, in this study, we propose a three-layer railway freight transport network system, analyze its hierarchical structure and describe the respective function orientation of the railway freight stations in different layers. Then we design a BP neural network model with adaptive learning algorithm and momentum BP algorithm to classify the railway freight stations into three layers. Finally, an empirical case study is presented to test the feasibility of the BP neural network. The simulation result indicates that the BP neural network model can classify the railway freight stations into three layers under relatively high training accuracy.

scholarly journals Clinicopathological Features of Fibrosarcomatous Dermatofibrosarcoma Protuberans and the Construction of a Back-Propagation Neural Network Recognition Model

2020 ◽  
Author(s):  
Yanan Li ◽  
Jiaqi Liang ◽  
Xuewen Xu ◽  
Xian Jiang ◽  
Chuan Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Classification Accuracy ◽  
Back Propagation ◽  
Dermatofibrosarcoma Protuberans ◽  
Clinicopathological Features ◽  
Recognition Model ◽  
Bp Neural Network Model

Abstract BackgroundFibrosarcomatous dermatofibrosarcoma protuberans (FS-DFSP) is a form of tumor progression of dermatofibrosarcoma protuberans (DFSP) with an increased risk of metastasis and recurrence. Few studies have compared the clinicopathological features of FS-DFSP and conventional DFSP (C-DFSP).ObjectivesTo better understand the epidemiological and clinicopathological characteristics of FS-DFSP.MethodsWe conducted a cohort study of 221 patients diagnosed with DFSP and built a recognition model with a back-propagation (BP) neural network for FS-DFSP.ResultsTwenty-six patients with FS-DFSP and 195 patients with C-DFSP were included. There were no differences between FS-DFSP and C-DFSP regarding age at presentation, age at diagnosis, sex, size at diagnosis, size at presentation, and the size interval. The negative ratio of CD34 in FS-DFSP (11.5%) was significantly lower than that in C-DFSP (5.1%) (P=0.005). The average Ki-67 index of FS-DFSP (18.1%) cases was significantly higher than that of C-DFSP (8.1%) cases (P<0.001). The classification accuracy of the BP neural network model training samples was 100%. The correct rates of classification and misdiagnosis were 84.1% and 15.9%.ConclusionsThe clinical manifestations of FS-DFSP and C-DFSP are similar but have large differences in immunohistochemistry. The classification accuracy and feasibility of the BP neural network model are high in FS-DFSP.

Study on Deformation Property of Soft Soil Based on Neural Networks

2013 ◽  
Vol 353-356 ◽  
pp. 270-273
Author(s):  
Yong Jian Liu ◽  
Zhang Ming Li ◽  
Yin Wang ◽  
Yi Mei Liu ◽  
Yong Jian Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Adaptive Learning ◽  
Bp Neural Network ◽  
Deformation Property ◽  
Soft Soil ◽  
Nonlinear Creep ◽  
Propagation Network ◽  
Bp Neural Network Model

Based on the triaxial test results of soft soils, an error back propagation network predicting model for deformation property of soft soil is built. Improved BP neural network model is trained by additional momentum term, adaptive learning rate and Bayesian regularization performance function. Research shows that improved BP neural network model applied to predict soft soil foundation settlement, has fast computation, high accuracy, strong generalization ability, and good capability of matching the real data and the measured one. According to test data, the creep models can avoid any artificial assumption of complex constitutive equation, and can reflect nonlinear creep properties of soft soil objectively, thus has better fault-tolerance and more convenient than the traditional method.

The Application of BP Neural Network in Settlement Prediction of Highway Soft Foundation

2011 ◽  
Vol 250-253 ◽  
pp. 3440-3443
Author(s):  
Yi Xue ◽  
Zheng Zheng Cao ◽  
Shan Liu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Linear Mapping ◽  
Point Method ◽  
Learning Ability ◽  
Settlement Prediction ◽  
Soft Foundation ◽  
Bp Neural Network Model

In view of the settlement of highway soft foundation, the paper proposes a method to predict soft foundation settlement based on BP neural network model, taking advantage of the strong non-linear mapping and learning ability of BP neural network. Then it is compared with the three-point method, obtaining some useful conclusions. Since the BP neural network model is directly based on real samples, it could avoid the mistakes due to factitiousness in the three-point method. It is proved that the BP neural network model is accurate and the settlement has least error.

scholarly journals Evaluation of Chinese Enterprise Safety Production Resilience Based on a Combined Gray Relevancy and BP Neural Network Model

2019 ◽  
Vol 11 (16) ◽  
pp. 4321 ◽  
Author(s):  
Jingjing Pei ◽  
Wen Liu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Three Dimensions ◽  
Gray Relational Analysis ◽  
Safety Production ◽  
Neural Network Prediction ◽  
Bp Neural Network Model

Improving the resilience of enterprise safety production is one of the important ways to deal with the frequency of safety accidents. Based on the definition of enterprise safety production resilience, we fully consider the impacts of recovery resilience, self-organizing resilience, and learning resilience as the three dimensions of enterprise safety production resilience. We build a back propagation (BP) neural network model that analyzes the main factors of enterprise safety production resilience using the results of gray relational analysis as an input that can assess the resilience of enterprise safety production and provide a valuable reference for the improvement of an enterprise’s safety production level. The results show that the resilience of production safety obviously increased after the Chinese enterprises with low resilience (as predicted by the model) adopted the corresponding early warning methods. The gray relational degree analysis method can incorporate well the variables for the establishment of the BP neural network prediction model.

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