A BP Neural Network-Based Automatic Windshield Wiper Controller

2012 ◽  
Vol 482-484 ◽  
pp. 31-34
Author(s):  
Hang Fei Xu ◽  
Hong Yan Chen ◽  
Kun Yuan
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Pattern Recognition ◽  
Bp Neural Network ◽  
Control Model ◽  
Windshield Wiper ◽  
Model Based

This paper introduces a method of constructing the control model of automatic windshield wiper based on BP neural network. A model of pattern recognition based on BP neural network is built and train it with specialists’ experience data, and then tested it. The result indicates that this model based on BP neural network is effective to handle uncertainties and nonlinearities of the automatic windshield wiper system, without use of a sophisticated mathematical model.

Study on the Generalized Holo-Factors Mathematical Model of Dimension-Error and Shape-Error for Sheet Metal in Stamping Based on the Back Propagation (BP) Neural Network

2016 ◽  
Vol 138 (6) ◽  
Author(s):  
Lizhi Gu ◽  
Tianqing Zheng
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Sheet Metal ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Shape Error ◽  
Back Propagation Algorithm ◽  
Minor Strain ◽  
Major Strain ◽  
Forming Precision

Precision improvement in sheet metal stamping has been the concern that the stamping researchers have engaged in. In order to improve the forming precision of sheet metal in stamping, this paper devoted to establish the generalized holo-factors mathematical model of dimension-error and shape-error for sheet metal in stamping based on BP neural network. Factors influencing the forming precision of stamping sheet metal were divided, altogether ten factors, and the generalized holo-factors mathematical model of dimension-error and shape-error for sheet metal in stamping was established using the back-propagation algorithm of error based on BP neural network. The undetermined coefficients of the model previously established were soluble according to the simulation data of sheet punching combined with the specific shape based on the BP neural network. With this mathematical model, the forecast data compared with the validate data could be obtained, so as to verify the fine practicability that the previously established mathematical model had, and then, it was shown that the generalized holo-factors mathematical model of size error and shape-error had fine practicality and versatility. Based on the generalized holo-factors mathematical model of error exemplified by the cylindrical parts, a group of process parameters could be selected, in which forming thickness was between 0.713 mm and 1.335 mm, major strain was between 0.085 and 0.519, and minor strain was between −0.596 and 0.319 from the generalized holo-factors mathematical model prediction, at the same time, the forming thickness, the major strain, and the minor strain were in good condition.

Temperature Prediction at the Blow End of the Converter via Control Model

2014 ◽  
Vol 1025-1026 ◽  
pp. 298-301
Author(s):  
Alexandre Furtado Ferreira
Keyword(s):  
Mathematical Model ◽  
Dynamic Control ◽  
Control Model ◽  
Mass Balances ◽  
Temperature Prediction ◽  
Oxygen Blow ◽  
Model Based ◽  
Gas Control ◽  
Prediction Capability

In the present work, a technique and model for temperature prediction at the blow end are briefly discussed, along with their limitations and perspectives for application. As a result of this analysis, a mathematical model based in heat and mass balances has been developed with a view to evaluating the possibility of improving this prediction capability. The study here presented focuses the development of a semi-dynamic control model in the LD-KGC converter (Linz-Donawitz-Kawasaki Gas Control Converter). The control model enables one to predict the temperature of the blow end by solving both the energy and mass equations. The inputs to the control model are the load data of the LD-KGC converter at the blow beginning and the collected data by the lance to 89% of oxygen blow. The results obtained in the present work were compared to the data measured in steelmaking. The semi-dynamic control model results agree well with data for LD-KGC converters.

The Research of Railway Passenger Flow Prediction Model Based on BP Neural Network

2012 ◽  
Vol 605-607 ◽  
pp. 2366-2369 ◽  
Author(s):  
Yao Wang ◽  
Dan Zheng ◽  
Shi Min Luo ◽  
Dong Ming Zhan ◽  
Peng Nie
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Good Effect ◽  
Passenger Flow ◽  
Model Based ◽  
Flow Prediction ◽  
Transport Organization ◽  
Railway Passenger ◽  
Short Time

Based on analyzing the principle of BP neural network and time sequence characteristics of railway passenger flow, the forecast model of railway short-term passenger flow based on BP neural network was established. This paper mainly researches on fluctuation characteristics and short-time forecast of holiday passenger flow. Through analysis of passenger flow and then be used in passenger flow forecasting in order to guide the transport organization program especially the train plan of extra passenger train. And the result shows the forecast model based on BP neural network has a good effect on railway passenger flow prediction.

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