Prediction of the Cutting Depth of Abrasive Suspension Jet Using a BP Artificial Neural Network

2012 ◽  
Vol 500 ◽  
pp. 249-252 ◽  
Author(s):  
Xiao Jian Liu ◽  
Qian Qian Fan ◽  
Yan Xia Feng
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Cutting Depth ◽  
Bp Artificial Neural Network ◽  
The Mean ◽  
Artificial Neural ◽  
Model Training ◽  
Abrasive Suspension

Abrasive suspension jet is a new embranchment of abrasive jet. In the cutting process of this jet, the suspension concentration is constant, so the cutting quality is more stable. In this paper, a prediction model based on a back-propagation (BP) artificial neural network is presented for predicting the cutting depth generated by abrasive suspension jet. In the application of the BP neural network, the mean error of the output in the model training is 0.01, the relatively discrepancy is below 8.70%. The modeling method based on the BP neural network is much more convenient and exact compared with traditional methods, and can always achieve a much better prediction effect. It is verified with experiments to be reasonable and feasible, and it is the better foundation for the future study of abrasive suspension jet.

scholarly journals RESEARCH ON INVERSION OF LIDAR EQUATION BASED ON NEURAL NETWORK

2019 ◽  
Vol XLII-3/W9 ◽  
pp. 171-176
Author(s):  
X. K. Wang ◽  
H. Zhao ◽  
H. L. Zhang ◽  
Y. P. Liu ◽  
C. Shu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Traditional Method ◽  
Back Propagation ◽  
Physical Parameters ◽  
Backscattering Coefficient ◽  
Bp Artificial Neural Network ◽  
Artificial Neural ◽  
Lidar Equation ◽  
Bp Neural Network Model

Abstract. Lidar is an advanced atmospheric and meteorological monitoring instrument. The atmospheric aerosol physical parameters can be acquired through inversion of lidar signals. However, traditional methods of solving lidar equations require many assumptions and cannot get accurate analytical solutions. In order to solve this problem, a method of inverting lidar equation using artificial neural network is proposed. This method is based on BP (Back Propagation) artificial neural network, the weights and thresholds of BP artificial neural network is optimized by Genetic Algorithm. The lidar equation inversion prediction model is established. The actual lidar detection signals are inversed using this method, and the results are compared with the traditional method. The result shows that the extinction coefficient and backscattering coefficient inverted by the GA-based BP neural network model are accurate than that inverted by traditional method, the relative error is below 4%. This method can solve the problem of complicated calculation process, as while as providing a new method for the inversion of lidar equations.

scholarly journals Piston Error Measurement for Segmented Telescopes with an Artificial Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3364
Author(s):  
Dan Yue ◽  
Yihao He ◽  
Yushuang Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Error Detection ◽  
Back Propagation ◽  
Good Precision ◽  
Modulation Transfer ◽  
Detection Range ◽  
Bp Artificial Neural Network ◽  
Artificial Neural ◽  
Trained Network

A piston error detection method is proposed based on the broadband intensity distribution on the image plane using a back-propagation (BP) artificial neural network. By setting a mask with a sparse circular clear multi-subaperture configuration in the exit pupil plane of a segmented telescope to fragment the pupil, the relation between the piston error of segments and amplitude of the modulation transfer function (MTF) sidelobes is strictly derived according to the Fourier optics principle. Then the BP artificial neural network is utilized to establish the mapping relation between them, where the amplitudes of the MTF sidelobes directly calculated from theoretical relationship and the introduced piston errors are used as inputs and outputs respectively to train the network. With the well trained-network, the piston errors are measured to a good precision using one in-focused broadband image without defocus division as input, and the capture range achieving the coherence length of the broadband light is available. Adequate simulations demonstrate the effectiveness and accuracy of the proposed method; the results show that the trained network has high measurement accuracy, wide detection range, quite good noise immunity and generalization ability. This method provides a feasible and easily implemented way to measure piston error and can simultaneously detect the multiple piston errors of the entire aperture of the segmented telescope.

Forecasting Coconut Yield: A Comparative Study between the Use of Traditional Forecasting and Feed Forward Back Propagation Artificial Neural Network

10.17158/320 ◽  
2014 ◽  
Vol 18 (2) ◽  
Author(s):  
Eric John G. Emberda ◽  
Den Ryan L. Dumas ◽  
Timothy Pierce M. Rentillo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Ann Model ◽  
Feed Forward ◽  
Feed Forward Back Propagation ◽  
Forecast Data ◽  
The Mean ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

<p>This study compared the use of Linear Regression and Feed Forward Backpropagation Artificial Neural Network (ANN) in forecasting the coconut yield and copra yield of a selected area in Davao region. Raw data were gathered from the Philippine Coconut Authority, Davao Research Center. An ANN model was created and tested repeatedly to the best combination of nodes. Accuracy of the forecast between the two methods was compared by looking at the mean square error and the standard error for variable x and y. Results showed that the use of Feed Forward Back Propagation Artificial Neural Network gives better accuracy of the forecast data.</p>

PREDICTION OF THE GLASS TRANSITION TEMPERATURES FOR POLYMERS WITH ARTIFICIAL NEURAL NETWORK

2008 ◽  
Vol 07 (05) ◽  
pp. 953-963 ◽  
Author(s):  
XIN-LIANG YU ◽  
BING YI ◽  
XUE-YE WANG
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Glass Transition ◽  
Bp Neural Network ◽  
Linear Regression Analysis ◽  
Back Propagation ◽  
Structure Property ◽  
Qspr Model ◽  
Rms Error ◽  
Artificial Neural

The glass transition temperature (Tg) values of three classes of vinyl polymers, i.e. polystyrenes, polyacrylates, and polymethacrylates, were predicted by using a quantitative structure–property relationship (QSPR) model constructed by back-propagation (BP) neural network. The four descriptors (the rigidness descriptor R HR resulted by hydrogen-bonding moieties group and/or rings, the chain mobility n, the molecular average polarizability α, the net charge of the most negative atom q-) were obtained directly from the polymers' monomer structures. Stepwise multiple linear regression analysis (MLRA) and artificial neural network (ANN) were used to generate the model. Simulated with the final optimum BP neural network [4–2–1], the results showed that the predicted Tg values were in good agreement with the experimental data, with a training set root-mean-square (rms) error of 20.478 K (R = 0.955) and a prediction set rms error of 20.174 K (R = 0.955).

scholarly journals Research on Prediction Model of Hotels’ Development Scale Based on BP Artificial Neural Network Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Nan Zhao ◽  
Sang-Bing Tsai
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Influencing Factors ◽  
Bp Neural Network ◽  
Tourism Development ◽  
Development Economic ◽  
Bp Artificial Neural Network ◽  
Relationship Analysis ◽  
Artificial Neural

Due to the lack of macro and systematic data, the target cost of high-star hotel project cannot meet the characteristics and needs of the hotel project itself. Therefore, the establishment of star hotel development scale prediction is urgent. In the scale development strategy, based on the previous studies, combined with the development characteristics of regional high-star hotels in a city, this paper constructs the index system of influencing factors of the development scale of high-star hotels and extracts the main influencing factors of hotel development scale by principal component analysis and partial relationship analysis, which are mainly urban development, economic development, tourism development, tourism development exhibition industry development, business development, and transportation development. The BP artificial neural network prediction method is used to establish a prediction model for the development scale of high-star hotels, by adopting the above key extraction factors as input of BP neural network. Through the input and output of the scale influence index data, the development scale of star hotels is accurately predicted. The simulation results verify the effectiveness and reliability of the star hotel development scale prediction strategy based on BP neural network, in terms of accuracy and model superiority.

An Integrated Predicting Model of New-Built Regional Logistics Center’s Demand Based on the Artificial Neural Network

2012 ◽  
Vol 253-255 ◽  
pp. 1512-1517
Author(s):  
Jian Feng Luo ◽  
Tian Shan Ma
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Factor Analysis ◽  
Back Propagation ◽  
Demand Forecast ◽  
Logistics Center ◽  
Bp Artificial Neural Network ◽  
Regional Logistics ◽  
Artificial Neural ◽  
Logistics Demand

In order to predict the scale of logistics demand for a new-built regional center, economic indicators and the other related measuring indicator of the scale for logistics demand is studied. The factor analysis and back propagation (BP) artificial neural network theory are applied to set up a model for predicting the scale of the logistics center’ s demand. The factor analysis is applied to this model to reduce the number of indicators of the input layer in the BP artificial neural network, and to reduce complexity. Then model is introduced to fit historical data of the scale of new –built a regional logistics center’ s demand .Finally,a third-layer BP artificial neural network is constructed. This model was applied to predict the scale of the logistics demand in an example and the forecasting result shows that forecasting accuracy of the model is good. It also provides a new way of a new-built regional logistics center’ s demand forecast.

scholarly journals Methodology for Developing Hydrological Models Based on an Artificial Neural Network to Establish an Early Warning System in Small Catchments

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Ivana Sušanj ◽  
Nevenka Ožanić ◽  
Ivan Marović
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Small Scale ◽  
Ann Model ◽  
Data Value ◽  
The Mean ◽  
Artificial Neural

In some situations, there is no possibility of hazard mitigation, especially if the hazard is induced by water. Thus, it is important to prevent consequences via an early warning system (EWS) to announce the possible occurrence of a hazard. The aim and objective of this paper are to investigate the possibility of implementing an EWS in a small-scale catchment and to develop a methodology for developing a hydrological prediction model based on an artificial neural network (ANN) as an essential part of the EWS. The methodology is implemented in the case study of the Slani Potok catchment, which is historically recognized as a hazard-prone area, by establishing continuous monitoring of meteorological and hydrological parameters to collect data for the training, validation, and evaluation of the prediction capabilities of the ANN model. The model is validated and evaluated by visual and common calculation approaches and a new evaluation for the assessment. This new evaluation is proposed based on the separation of the observed data into classes based on the mean data value and the percentages of classes above or below the mean data value as well as on the performance of the mean absolute error.

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