Evaluation of road condition based on BA-BP algorithm

2021 ◽  
Vol 40 (1) ◽  
pp. 331-348
Author(s):  
Dongyao Jia ◽  
Chuanwang Zhang ◽  
Dandan Lv
Keyword(s):  
Large Scale ◽  
Evaluation Method ◽  
Back Propagation ◽  
Bat Algorithm ◽  
Back Propagation Algorithm ◽  
Bp Network ◽  
Condition Evaluation ◽  
Lower Accuracy ◽  
Road Condition ◽  
Bp Model

BP (Back Propagation) neural network has been widely applied for classification tasks including road condition evaluation, however, BP model has the problem of lower accuracy and slow convergence rate. A novel road condition evaluation method based on BA-BP (Bat-Back Propagation) algorithm is proposed for the unstructured small road condition evaluation, which filled the vacancy of specific small road scenes. Firstly, five kinds of road condition features including roughness, curvature, obstacle width to height ratio, obstacle effective area ratio, obstacle coefficient are defined and extracted. Then obstacles from region of interest (ROI) in front of the vehicle are analyzed. Finally, Bat algorithm is used to optimize the searching of initial network weights and thresholds, which obtained a higher accuracy of 95.15% and efficient training process. Comparison experiments showed that the proposed approach improved the accuracy with 5.31%, 3.32%, 3.17% than the BP, GA-BP and FA-BP model, respectively. As for the processing time of collected road data, BA-BP network consumed less time of 2 s and 3.9 s compared with GA-BP and FA-BP. Proposed method also outperformed than most of the state-of-the-art approaches with higher accuracy and simpler hardware environments, which proved its potential of being popularized in large scale real-time systems.

The Use of BP Neural Network in Evaluation about the Appearance of the Garments

2012 ◽  
Vol 433-440 ◽  
pp. 4320-4323 ◽  
Author(s):  
Jing Wang ◽  
Jin Ying Song ◽  
Ai Qing Tang
Keyword(s):  
Neural Network ◽  
Principal Components ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Original Data ◽  
Back Propagation Algorithm ◽  
Bp Network ◽  
Self Adaptation ◽  
Hidden Layer ◽  
Good Agreement

This article reports the use of BP neural network for evaluation of the appearance of garment after dry wash. The selected data about parameters of fabrics and interlinings are analyzed by principal analysis and eight principal components are obtained through this method. A BP neural network with a single hidden layer is constructed including eight input nodes, six hidden nodes and one output nodes. During training the network with a back-propagation algorithm, the eight principal components are used as input parameters, while the rate of the appearance of the garment are used as output parameters. The weight values are modified with momentum and learning rate self-adaptation to solve the two defects of the BP network. All original data are preprocessed and the learning process is successful in achieving a global error minimum. The rate of the appearance can be evaluated with this training network and there is a good agreement between the evaluated and tested values.

Study on Back-Propagation Neural Networks in Hydrological Forecast

2014 ◽  
Vol 687-691 ◽  
pp. 2153-2156
Author(s):  
Ri Jun Zhang ◽  
Zhong Sheng Li
Keyword(s):  
Neural Networks ◽  
Traditional Method ◽  
Back Propagation ◽  
Projection Pursuit ◽  
Forecasting Model ◽  
Bp Network ◽  
Hydrological Forecasting ◽  
New Methods ◽  
Hydrological Forecast ◽  
Bp Model

The hydrological forecasting model are established respectively by the traditional method and the new methods, BP network and projection pursuit, in order to study the feasibility and practicality. The result shows that the accuracy of the BP model is within 10%, has better forecasting effect and more practical value than the others.

Application of the Bat Algorithm to Optimize the BP Neural Network

2014 ◽  
Vol 721 ◽  
pp. 531-534
Author(s):  
Hui Hui Xiao ◽  
Yan Ming Duan
Keyword(s):  
Global Optimization ◽  
Extreme Values ◽  
Back Propagation ◽  
Bat Algorithm ◽  
Bp Algorithm ◽  
Learning Ability ◽  
Simulation Test ◽  
Bp Network ◽  
Error Back Propagation ◽  
Local Extreme

For the standard BP algorithm usually has the limitations of slow convergence and local extreme values, a new method to adjust weights of BP network was proposed based on the bat algorithm of the global optimization ability and the strong convergence. The new algorithm was based on the weight adjustments of error back propagation of BP algorithm and the weight and threshold of BP network modification using the bats position update. The new algorithm can not only use the bat ability of global optimization, but also contain the feature of error back propagation of BP algorithm. The new algorithm was used in simulation test of two typical functions, results of which were analyzed and compared with that of basic BP algorithm and PSO-BP algorithm. Experimental results show that the new algorithm has faster convergence speed and higher convergence accuracy, and improved the learning ability and generalization ability of BP network. The performances of the new algorithm were superior to that of other 2 kinds of BP network algorithm.

Study on the Forecast Model of Water Quantity on the Basis of BP Artificial Neural Network

2013 ◽  
Vol 475-476 ◽  
pp. 188-191
Author(s):  
Xiao Bin Ding
Keyword(s):  
Water Flow ◽  
Yellow River ◽  
Back Propagation ◽  
Forecast Model ◽  
Bp Network ◽  
Bp Artificial Neural Network ◽  
Functional Fitting ◽  
Artificial Neural ◽  
Propagation Network ◽  
Bp Model

Back Propagation network, Widely used in automatic control, image recognition, hydrological forecasting and water quality evaluation, etc., as one of the Artificial Neural Networks, has stronger property of mapping, classification, functional fitting. This article takes the water flow of Lanzhou section of Yellow river as example by use of BP model to predict the water flow. It is well proved that BP network model can reach the purposes of early warning and forecasting.

Investigation on the Condition Evaluation Method Based on NSET Model of the Propulsion System in Large Scale Ship

2019 ◽  
Author(s):  
Jinxin Zhao ◽  
Jian Zhou ◽  
Peng Shang ◽  
Yinxun Zhang
Keyword(s):  
State Estimation ◽  
Large Scale ◽  
Evaluation Method ◽  
Propulsion System ◽  
Sensor Calibration ◽  
Operating Characteristics ◽  
Nonlinear State Estimation ◽  
Condition Evaluation ◽  
Wide Range ◽  
Nonlinear State

Abstract The condition of the propulsion system in large scale ship directly affects the quality and safety of the ship’s operation, so it is important to evaluate the condition of the propulsion system. The Nonlinear State Estimation Technology (NSET) method is a nonparametric modeling method, which is mostly used in nuclear power plant sensor calibration, equipment monitoring, and electronic product life prediction. In this paper, the nonlinear state estimation technology (NSET) method is applied to the condition evaluation of the propulsion system. According to the operating characteristics and state parameters of the propulsion system, this paper first performs data preprocessing, and then uses the prepossessed data to perform condition evaluation by the NSET method. The model method can be connected with the database system. It has the advantages of faster evaluation speed and higher precision, and can detect possible failures of equipment system in time. This method has a wide range of adaptability.

scholarly journals An Integrated Evaluation Method of the Wind Power Ramp Event Based on Generalized Information of the Source, Grid, and Load

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6503
Author(s):  
Jie Wan ◽  
Yanjia Wang ◽  
Guorui Ren ◽  
Jinfu Liu ◽  
Wei Wang ◽  
...  
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Back Propagation ◽  
Basic Research ◽  
Assessment Method ◽  
Frequency Change ◽  
Short Period ◽  
Generalized Information

The wind power ramp event includes large fluctuations in wind power within a short period of time. To maintain grid stability, defining, identifying, and predicting the wind power ramp event is inevitable. Therefore, a comprehensive assessment method of wind power ramp events that combines the generalized information of the source, grid, and load sides is proposed. In this method, we put forward a channel self-selected multi-layer coefficient correction model (CSMCC) and wind power ramp threshold, according to the allowable value of a grid frequency change. Additionally, the availability of data-driven modeling methods is verified by performing autocorrelation analysis. Finally, the comprehensive evaluation method, which combines the back propagation (BP) neural network, supports the vector machine and CSMCC model is proved to be effective. This paper has a certain reference significance for basic research on large-scale wind power safety and efficient utilization.

scholarly journals Predicting Tunnel Squeezing Using the SVM-BP Combination Model

2021 ◽  
Author(s):  
Zhen HUANG ◽  
Minxing Liao ◽  
Haoliang Zhang ◽  
Jiabing Zhang ◽  
Shaokun Ma ◽  
...  
Keyword(s):  
Large Scale ◽  
Back Propagation ◽  
Classification Performance ◽  
Propagation Model ◽  
Support Vector ◽  
Characteristic Parameters ◽  
Svm Model ◽  
Tunnel Diameter ◽  
Squeezing Prediction ◽  
Bp Model

Abstract Rock squeezing has a large influence on tunnel construction safety; thus, when designing and constructing tunnels it is highly important to use a reliable method for predicting tunnel squeezing from incomplete data. In this study, a combination SVM-BP (support vector machine-back-propagation) model is proposed to classify the deformation caused by surrounding rock squeezing. We designed different characteristic parameters and three types of classifiers (an SVM model, a BP model, and the proposed SVM-BP model) for the tunnel-squeezing prediction experiments and analysed the accuracy of predictions by different models and the influences of characteristic parameters on the prediction results. In contrast to other prediction methods, the proposed SVM-BP model is verified to be reliable. The results show that four characteristics: tunnel diameter (D), tunnel buried depth (H), rock quality index (Q) and support stiffness (K) reflect the effect of rock squeezing sufficiently for classification. The SVM-BP model combines the advantages of both an SVM and a BP neural network. It possesses flexible nonlinear modelling ability and the ability to perform parallel processing of large-scale information. Therefore, the SVM-BP model achieves better classification performance than do the SVM or BP models separately. Moreover, coupling D, H, and K has a significant impact on the predicted results of tunnel squeezing.

scholarly journals Network Optimization Based Lane Line Classification Study

2021 ◽  
Author(s):  
Hai Wang ◽  
Pengyi Deng ◽  
Shulei Sun ◽  
Guoying Tian ◽  
Shuguang Li ◽  
...  
Keyword(s):  
Quality Evaluation ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Back Propagation ◽  
Middle Layer ◽  
Analytic Hierarchy ◽  
Bp Network ◽  
Comprehensive Evaluation Method ◽  
Lane Line ◽  
Hierarchy Process

Efficient quality evaluation provides support for the timely and good maintenance of the lane line marking. This paper searches and optimizes the back propagation(BP) network model which referred to the analytic hierarchy process(AHP) model structure, as well as the number of nodes in the middle layer network. Based on this, a comprehensive evaluation method of multi-dimensional lane line quality such as shape, color and contrast is established. The experimental results show that the parameters of the model are more simplified, and the scoring and classification results of lane lines are more accurate.

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

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