scholarly journals A Neural Network Model for Driver’s Lane-Changing Trajectory Prediction in Urban Traffic Flow

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Chenxi Ding ◽  
Wuhong Wang ◽  
Xiao Wang ◽  
Martin Baumann
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Driving Simulator ◽  
Back Propagation ◽  
Urban Traffic ◽  
Lane Changing ◽  
Training Time

The neural network may learn and incorporate the uncertainties to predict the driver’s lane-changing behavior more accurately. In this paper, we will discuss in detail the effectiveness of Back-Propagation (BP) neural network for prediction of lane-changing trajectory based on the past vehicle data and compare the results between BP neural network model and Elman Network model in terms of the training time and accuracy. Driving simulator data and NGSIM data were processed by a smooth method and then used to validate the availability of the model. The test results indicate that BP neural network might be an accurate prediction of driver’s lane-changing behavior in urban traffic flow. The objective of this paper is to show the usefulness of BP neural network in prediction of lane-changing process and confirm that the vehicle trajectory is influenced previously by the collected data.

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.

scholarly journals Technical and tactical diagnosis model of table tennis matches based on BP neural network

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Wenwen Huang ◽  
Miaomiao Lu ◽  
Yuxuan Zeng ◽  
Mengyue Hu ◽  
Yi Xiao
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Evaluation Method ◽  
Back Propagation ◽  
Predictive Ability ◽  
Diagnostic Model ◽  
Table Tennis ◽  
Diagnosis Model

Abstract Background The technical and tactical diagnosis of table tennis is extremely important in the preparation for competition which is complicated by an apparent nonlinear relationship between athletes’ performance and their sports quality. The neural network model provides a high nonlinear dynamic processing ability and fitting accuracy that may assist in the diagnosis of table tennis players’ technical and tactical skill. The main purpose of this study was to establish a technical and tactical diagnosis model of table tennis matches based on a neural network to analyze the influence of athletes’ techniques and tactics on the competition results. Methods A three-layer Back Propagation (BP) neural network model for table tennis match diagnosis were established. A Double Three-Phase evaluation method produced 30 indices that were closely related to winning table tennis matches. A data sample of 100 table tennis matches was used to establish the diagnostic model (n = 70) and evaluate the predictive ability of the model (n = 30). Results The technical and tactical diagnosis model of table tennis matches based on BP neural network had a high-level of prediction accuracy (up to 99.997%) and highly efficient in fitting (R2 = 0.99). Specifically, the technical and tactical diagnosis results indicated that the scoring rate of the fourth stroke of Harimoto had the greatest influence on the winning probability. Conclusion The technical and tactical diagnosis model of table tennis matches based on BP neural network was highly accurate and efficiently fit. It appears that the use of the model can calculate athletes’ technical and tactical indices and their influence on the probability of winning table tennis matches. This, in turn, can provide a valuable tool for formulating player’s targeted training plans.

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.

scholarly journals A Back Propagation Neural Network Model Optimized by Mind Evolutionary Algorithm for Estimating Cd, Cr, and Pb Concentrations in Soils Using Vis-NIR Diffuse Reflectance Spectroscopy

2019 ◽  
Vol 10 (1) ◽  
pp. 51 ◽  
Author(s):  
Xi Wang ◽  
Shi An ◽  
Yaqing Xu ◽  
Huping Hou ◽  
Fuyao Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Heavy Metal ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Metal Content ◽  
Diffuse Reflectance Spectroscopy ◽  
Back Propagation ◽  
Heavy Metal Content ◽  
Estimation Accuracy

Visible and near infrared spectroscopy is an effective method for monitoring the content of heavy metals in soil. However, due to the difference between polluted soil with phytoremediation and without phytoremediation, the common estimation model cannot meet accuracy requirements. To solve this problem, combined with an ecological restoration experiment for soil contamination using the plant Neyraudia reynaudiana, this study explored the feasibility of using a hyperspectral technology to estimate the heavy metal content (Cd, Cr, and Pb) of soil under phytoremediation. A total of 108 surface soil samples (from depths of 0–20 cm) were collected. Inversion models were established using partial least squares regression (PLSR) and the back propagation neural network optimized by a mind evolutionary algorithm (MEA-BPNN). The results revealed that: (1) modeling with derivative-transformed spectra can effectively enhance the correlation between soil spectral reflectance and heavy metal content. (2) Compared with the BP neural network model, the estimation accuracy (R2) was improved from 0.728, 0.737, and 0.675 to 0.873, 0.884, and 0.857 using the MEA-BP neural network model. The residual prediction deviation (RPD) values for the three heavy metals Cd, Cr, and Pb using the MEA-BPNN model were 2.114, 3.000, and 2.560, respectively. Among them, the estimated model of Cd was an excellent prediction. (3) Compared with PLSR, the model prediction results established by the MEA-BP neural network had higher estimation accuracy. In summary, the use of diffuse reflectance spectroscopy to predict heavy metal content provides a theoretical basis for further study of the large-scale monitoring of soil heavy-metal pollution and its remediation evaluation in the polluted area, which is of great significance.

Application of Elm Neural Network in Dam Displacement Early Warning Model

2013 ◽  
Vol 864-867 ◽  
pp. 2363-2366
Author(s):  
Tu Tu ◽  
Feng Zhu ◽  
Ai Wu Cao ◽  
Lu He ◽  
Geng Ying
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Early Warning ◽  
Bp Neural Network ◽  
Training Time ◽  
Multiple Factors ◽  
Bp Neural Network Model ◽  
Warning Model ◽  
Temperature Water

The dam displacement is related to multiple factors such as time, temperature, water level and etc. And it presents a strong nonlinear and certain randomness.Neural network model because of its inherent characteristics can better simulate the dam displacement.Nowadays,It has methods to estimate the displacement of the dam by constructing physical model and BP neural network model.But BP neural network's training time is too long and the forecast effect is not very good.So this paper introduces Elm neural network model,establishs Elm neural network model of dam displacement early warning considering multiple factors to estimate the displacement.By a simple example and compared with BP neural network model to reflect the rationality and scientificity of this method.

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