scholarly journals An Improved Rollover Index Based on BP Neural Network for Hydropneumatic Suspension Vehicles

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Xiaotong Dong ◽  
Yi Jiang ◽  
Zhou Zhong ◽  
Wei Zeng ◽  
Wei Liu
Keyword(s):  
Neural Network ◽  
Inclination Angle ◽  
Bp Neural Network ◽  
Warning System ◽  
Bp Network ◽  
The Road ◽  
Vehicle Test ◽  
Rollover Index ◽  
Hidden Layer ◽  
Good Agreement

The 3-DOF rollover model has been established by the Lagrangian second-class equation, taking the road inclination angle, the steering strategy, and the hydropneumatic suspension characteristics into consideration. A 3-layer BP (backpropagation) neural network is applied to predict the road inclination angle and to optimize the rollover model in real-time. The number of the hidden layer neurons for the BP network is also discussed. The numerical calculation of the optimized rollover model is in good agreement with the full-scale vehicle test. Different rollover indexes are compared, and the results indicate that the rollover index of dynamic LTR optimized by the BP neural network can evaluate the rollover tendency more accurately in the ramp steering test and the snake steering test. This study provides practical meanings for developing a rollover warning system.

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.

The Establishment of Land Appraisal Model in Xiamen Based on BP Neural Network

2012 ◽  
Vol 518-523 ◽  
pp. 6084-6087
Author(s):  
Qing Ye ◽  
Ya Yi Su ◽  
Fei Chen
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Network Theory ◽  
Evaluation Model ◽  
Land Evaluation ◽  
Bp Network ◽  
The Neural Network ◽  
Hidden Layer ◽  
Neural Network Theory ◽  
Error Index

Establish the land evaluation model of Xiamen by means of BP neural network theory, taking 2007-2009 land evaluation cases of Xiamen as examples. Through statistical analysis, we find that the neural network which has 9 net work hidden layer nodes and 19% of maximal error index is more suitable for Xiamen land price assessment than others. Empirical analysis shows that the model has a good generalization ability, which can be used for land evaluation practices. The results indicates that the properties of autonomous learning of BP network can reduce the subjective factors of appraiser in land evaluation , also, the network has the advantage of simple and quick calculation.

Thermal Error Modeling Based on Genetic Algorithm and BP Neural Network of High-Speed Spindle System

2015 ◽  
Author(s):  
Chi Ma ◽  
Liang Zhao ◽  
Hu Shi ◽  
Xuesong Mei ◽  
Jun Yang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Correlation Analysis ◽  
Bp Neural Network ◽  
High Speed ◽  
Thermal Error ◽  
Error Modeling ◽  
Bp Network ◽  
Error Models ◽  
Hidden Layer

In order to improve the prediction accuracy of the thermal error models, grey cluster grouping and correlation analysis were proposed to optimize and select the heat-sensitive points to improve the performances of the thermal error model and minimize the independent variables to reduce modeling cost. Subsequently, the neural network with back propagation (BP) algorithm was proposed to construct the strongly nonlinear mapping relationship between spindle thermal errors and typical temperature variables. However, the shortcomings of the BP network restricted the accuracy, robustness and convergence of thermal error models. Then, a genetic algorithm (GA), which regarded the reciprocal of the absolute value sum of the differences between the predicted and desired outputs as the number of nodes in the hidden layer, was proposed to optimize the structure and initial values of the network. And the number of the nodes in the hidden layer can be determined by performing such operations of GAs. Moreover, the reciprocal of the sum square of the difference between the predicted and expected outputs of individuals is regarded as the fitness function and the weights and thresholds of the BP neural network are optimized by setting the control parameters of GAs. Then, the elongation and thermal tilt angle models of high-speed spindles were proposed based on BP and GA-BP networks and the fitting and prediction abilities were compared. The results showed that the grey cluster grouping and correlation analysis could depress the multicollinearity among temperature variables and improve the stability and accuracy of the thermal error models. Moreover, although the traditional BP network had better fitting ability, its convergence and generality were far worse than the GA-BP model and it is more suitable to use the GA-BP neural network as the thermal error modeling method in the compensation system.

Application of BP Neural Network in Products Forecast

2014 ◽  
Vol 644-650 ◽  
pp. 2455-2458
Author(s):  
Cai Xia Liu
Keyword(s):  
Neural Network ◽  
Parallel Processing ◽  
Bp Neural Network ◽  
Structural Model ◽  
Learning Rate ◽  
Practical Significance ◽  
Nonlinear Mapping ◽  
Bp Network ◽  
Hidden Layer ◽  
Design Ideas

BP neural network has parallel processing capabilities and a good approximation of the nonlinear mapping and gradually been widely used in the forecast. Because it is difficult to determine the BP neural network structural model, this paper presents the design ideas from six aspects. Combined with the practical example-mushroom classification, this paper presents the affect of the hidden layer, learning rate, training function on BP network and has some practical significance.

scholarly journals Prediction of Shear Characteristics of Unsaturated Soil Based on BP Neural Network

2020 ◽  
Vol 165 ◽  
pp. 03034
Author(s):  
Jia Lei Liang ◽  
Guang Ri Jin ◽  
Zhi Xie Shen
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Unsaturated Soil ◽  
Bp Neural Network ◽  
Layer Structure ◽  
Network Models ◽  
Shear Strength Parameters ◽  
Bp Network ◽  
Shear Characteristics ◽  
Hidden Layer

The prediction model of shear strength parameters of unsaturated soil based on indoor test data is established by using BP neural network. Five kinds of network models with different number of hidden layer nodes are trained and studied, and the best network model is selected to conduct the prediction. The results show that the optimal BP network model is a single hidden layer structure of 8-16-2. Using this model to predict, the correlation coefficient and regression coefficient between the predicted value and the measured value are high, and the predicted result is reliable, so the method has certain practicability.

Study on the Relation between Waxberry Color and its Nutrition Composition Based on BP Neural Network

2011 ◽  
Vol 304 ◽  
pp. 268-273
Author(s):  
Hong Xia Zhao ◽  
Zhi Xia Liu ◽  
Zhi Yang Luo ◽  
Guan Yun Xiao
Keyword(s):  
Neural Network ◽  
Transfer Function ◽  
Linear Model ◽  
Bp Neural Network ◽  
Bp Network ◽  
The Neural Network ◽  
Farm Produce ◽  
Training Error ◽  
Hidden Layer ◽  
Function Transfer

The color of farm produce is a very important index of quality, its nutrition is correlative with itself color. At present, most of the analyses for pigment and nutrient composition still depend on chemical method; therefore the relation is studied between waxberry color and its nutrition composition based on BP neural network. The conversion relation is expressed by three-layer BP network, which hidden layer has 11 node numbers and its transfer function adopts tansig function; transfer function of output layer selects purelin function. The neural network and linear model of nutrition composition is compared respectively. The MSE value of linear model is 0.300892, and that training error of neural network is 0.0219585. From this result，we can find that the conversion relation between waxberry color and its nutrition composition is a complex non-linear relation, so neural network is adopted to complete this conversion.

Optimization of Characteristic Parameters of Pipeline Crack Identification Based on BP Neural Network

2014 ◽  
Vol 926-930 ◽  
pp. 3442-3446 ◽  
Author(s):  
Ya Chun Dai ◽  
Dian Kai Huang ◽  
Jian Wei Xu
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Bp Neural Network ◽  
Crack Identification ◽  
Characteristic Parameters ◽  
Emission Data ◽  
Bp Network ◽  
Emission Signal ◽  
Emission Parameter ◽  
Hidden Layer

Abstract. In this paper, we used three-layer BP network with a single hidden layer, and to design the structure of BP networks and set the parameters. We used the way of increasing the number of the hidden layer neurons and comparing the training errors and training number of the BP neural network to determine the number of the hidden layer neurons.Again, according to the acoustic emission data from the acquisition system and the designed BP neural network, we extract characteristic parameters of the corresponding crack acoustic emission signal,and to screen out seven acoustic emission parameter which the most represent crack characteristic by investigating each characteristic parameters' sensitivity of characterizing the crack condition, and according to the experiment data of the seven crack characteristic parameters to identify the crack state.

scholarly journals Rolling Bearing Fault Prediction Method Based on QPSO-BP Neural Network and Dempster–Shafer Evidence Theory

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1094 ◽  
Author(s):  
Lanjun Wan ◽  
Hongyang Li ◽  
Yiwei Chen ◽  
Changyun Li
Keyword(s):  
Neural Network ◽  
Working Conditions ◽  
Bp Neural Network ◽  
Evidence Theory ◽  
Prediction Method ◽  
Rolling Bearing ◽  
Fault Prediction ◽  
Fault Classification ◽  
Bearing Fault ◽  
Hidden Layer

To effectively predict the rolling bearing fault under different working conditions, a rolling bearing fault prediction method based on quantum particle swarm optimization (QPSO) backpropagation (BP) neural network and Dempster–Shafer evidence theory is proposed. First, the original vibration signals of rolling bearing are decomposed by three-layer wavelet packet, and the eigenvectors of different states of rolling bearing are constructed as input data of BP neural network. Second, the optimal number of hidden-layer nodes of BP neural network is automatically found by the dichotomy method to improve the efficiency of selecting the number of hidden-layer nodes. Third, the initial weights and thresholds of BP neural network are optimized by QPSO algorithm, which can improve the convergence speed and classification accuracy of BP neural network. Finally, the fault classification results of multiple QPSO-BP neural networks are fused by Dempster–Shafer evidence theory, and the final rolling bearing fault prediction model is obtained. The experiments demonstrate that different types of rolling bearing fault can be effectively and efficiently predicted under various working conditions.

Building Electronic Commerce Recommendation System Based on Ontology Learning and BP Neural Network

2013 ◽  
Vol 718-720 ◽  
pp. 1961-1966
Author(s):  
Hong Sheng Xu ◽  
Qing Tan
Keyword(s):  
Neural Network ◽  
Electronic Commerce ◽  
Bp Neural Network ◽  
Optimization Problem ◽  
Recommendation System ◽  
High Efficiency ◽  
Ontology Learning ◽  
The Neural Network ◽  
Network Process ◽  
Hidden Layer

Electronic commerce recommendation system can effectively retain user, prevent users from erosion, and improve e-commerce system sales. BP neural network using iterative operation, solving the weights of the neural network and close values to corresponding network process of learning and memory, to join the hidden layer nodes of the optimization problem of adjustable parameters increase. Ontology learning is the use of machine learning and statistical techniques, with automatic or semi-automatic way, from the existing data resources and obtaining desired body. The paper presents building electronic commerce recommendation system based on ontology learning and BP neural network. Experimental results show that the proposed algorithm has high efficiency.

scholarly journals An Air Quality Predictive Model of Licang of Qingdao City Based on BP Neural Network

2013 ◽  
Vol 756-759 ◽  
pp. 3366-3371 ◽  
Author(s):  
Ruo Bo Xin ◽  
Zhi Fang Jiang ◽  
Ning Li ◽  
Lu Jian Hou
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Predictive Model ◽  
Bp Neural Network ◽  
Ambient Air ◽  
Monitoring Site ◽  
Ambient Air Quality ◽  
Bp Network ◽  
Air Quality Forecast ◽  
Qingdao City

In order to obtain high precision results of urban air quality forecast, we propose a short-term predictive model of air quality in this paper, which is on the basis of the ambient air quality monitoring data and relevant meteorological data of a monitoring site in Licang district of Qingdao city in recent three years. The predictive model is based on BP neural network and used to predict the ambient air quality in the next some day or within a certain period of hours. In the design of the predictive model, we apply LM algorithm, Simulated Annealing algorithm and Early Stopping algorithm into BP network, and use a reasonable method to extract the historical data of two years as the training samples, which are the main reasons why the prediction results are better both in speed and in accuracy. And when predicting within a certain period of hours, we also adopt an average and equivalent idea to reduce the error accuracy, which brings us good results.

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