Computerized color separation system for printed fabrics by using backward-propagation neural network

2007 ◽  
Vol 8 (5) ◽  
pp. 529-536 ◽  
Author(s):  
Chung-Feng Jeffrey Kuo ◽  
Te-Li Su ◽  
Yi-Jen Huang
Keyword(s):  
Neural Network ◽  
Separation System ◽  
Color Separation ◽  
Backward Propagation

scholarly journals A backward propagation neural network for predicting daily transpiration of poplar

2007 ◽  
Vol 50 (5) ◽  
pp. 1277-1284 ◽  
Author(s):  
Yan Meijun ◽  
Yang Peiling ◽  
Ren Shumei ◽  
Luo Yuanpei ◽  
Xu Tingwu
Keyword(s):  
Neural Network ◽  
Daily Transpiration ◽  
Backward Propagation

scholarly journals Traffic marking recognition based on generating antagonistic neural network

2019 ◽  
Vol 136 ◽  
pp. 04076 ◽  
Author(s):  
Shuwei Xu ◽  
Shan Zhang ◽  
Shuwei Xu
Keyword(s):  
Neural Network ◽  
Markov Chain ◽  
High Accuracy ◽  
Experimental Results ◽  
Detection Methods ◽  
Image Detection ◽  
Fast Recognition ◽  
Repeated Sampling ◽  
Backward Propagation ◽  
Traditional Image

This paper presents a method of extracting traffic lines from image images by GAN. Compared with the traditional image detection methods, the counter neural network does not need repeated sampling of Markov chain and adopts the method of backward propagation. Therefore, when detecting the image, GAN do not need to be updated with samples; it can produce better quality samples, express more clearly. Experimental results show that the method has strong generalization ability, fast recognition speed and high accuracy.

Applying BP Neural Network Model to Forecast the Largest Coiling Torque of down Coilers

2014 ◽  
Vol 936 ◽  
pp. 1614-1619
Author(s):  
Ke Zhao ◽  
Zhi Gang Wang ◽  
Chang Ming Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
High Strength ◽  
Coiling Temperature ◽  
Strip Steel ◽  
Development And Utilization ◽  
Hot Rolling Mill ◽  
Bp Neural Network Model ◽  
Backward Propagation ◽  
Relationship Of

Down coiler is an important equipment of hot rolling mill. The coiling torque is changing constantly in the process of strip steel coiling, and the largest coiling torque depends on several factors, such as the material and specification of coiling strip, the coiling temperature and the process parameters and so on. Only when the largest coiling torque is less than the carrying capacity could the coiler work in security. A topology relationship of the largest coiling torque among the materials, the specification of the strip and the coiling temperature is established. Based on the BP(backward propagation of errors) artificial neural network, a predicted formula model of the largest coiling torque in coiling high strength strip is built, which provides a theoretical basis for the development and utilization of the largest working potential of the down coiler. Keywords: Down Coiler; BP Neural Network; Coiling Torque; Forecast

Prediction of Egg's Freshness Using Backward Propagation Neural Network

2011 ◽  
Vol 27 (2) ◽  
pp. 279-285 ◽  
Author(s):  
S. Dhakal ◽  
J. Wu ◽  
J. Chen ◽  
Y. Peng
Keyword(s):  
Neural Network ◽  
Backward Propagation

Prediction of Forming Load for the Helical Gear Precision Forging by BP Neural Network

2013 ◽  
Vol 823 ◽  
pp. 170-174
Author(s):  
Wei Feng ◽  
Ji Chang Cao ◽  
Shu Ting Wu ◽  
Yang Fan Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Fem Simulation ◽  
Helical Gear ◽  
The Finite Element Method ◽  
Forming Process ◽  
Forming Load ◽  
Precision Forging ◽  
Metal Forming Process ◽  
Backward Propagation

Precision forging of the helical gear is a complex metal forming process under coupled effects with multi-factors. The high forming load is required to fill the teeth corner, which significantly causes failure, plastic deformation and wear of dies. The maximum forming load during precision forging helical gear is calculated by the finite element method (FEM). Combining the FEM simulation results with the artificial neural networks (ANN), backward propagation (BP) neural network is trained using the data of FEM simulation as learning sample. The trained BP neural network is validated using test samples and used to predict the maximum forming load under the different deformation conditions. The results show that the predicted results agree well with the simulated ones, the differences of prediction results exhibit low value, the predicted precision satisfy the request of industry.

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