RESEARCH ON INVERSION OF LIDAR EQUATION BASED ON NEURAL NETWORK

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.

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Piston Error Measurement for Segmented Telescopes with an Artificial Neural Network

Sensors ◽

10.3390/s21103364 ◽

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.

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.500.249 ◽

2012 ◽

Vol 500 ◽

pp. 249-252 ◽

Cited By ~ 1

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.

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.253-255.1512 ◽

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.

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Implementation of Back Propagation Artificial Neural Network for Heart Disease Abnormality Diagnosis

Journal of Physics Conference Series ◽

10.1088/1742-6596/1764/1/012165 ◽

2021 ◽

Vol 1764 (1) ◽

pp. 012165

Author(s):

Jaya Kuncara Rosa Susila ◽

Muhammad Afit ◽

Pujo Laksono

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Heart Disease ◽

Back Propagation ◽

Artificial Neural

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Short circuit current forecast of large scale power grid based on improving BP Artificial Neural Network combined with Genetic Algorithm

2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies ◽

10.1109/drpt.2008.4523583 ◽

2008 ◽

Cited By ~ 1

Author(s):

Bo Liu ◽

Yan Zhang

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Large Scale ◽

Short Circuit ◽

Power Grid ◽

Short Circuit Current ◽

Bp Artificial Neural Network ◽

Artificial Neural ◽

Grid Based

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Test on Flood Prediction-Model Using Artificial Neural Network for ShiiLiAn Hydrologic Station on MinChiang,China

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.39.555 ◽

2010 ◽

Vol 39 ◽

pp. 555-561 ◽

Cited By ~ 1

Author(s):

Qing Hua Luan ◽

Yao Cheng ◽

Zha Xin Ima

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Flood Control ◽

Back Propagation ◽

Forecast Model ◽

Flood Forecast ◽

Back Propagation Algorithm ◽

Nonlinear Method ◽

Flood Prediction ◽

Artificial Neural

The establishing of a precise simulation model for runoff prediction in river with several tributaries is the difficulty of flood forecast, which is also one of the difficulties in hydrologic research. Due to the theory of Artificial Neural Network, using Back Propagation algorithm, the flood forecast model for ShiLiAn hydrologic station in Minjiang River is constructed and validated in this study. Through test, the result shows that the forecast accuracy is satisfied for all check standards of flood forecast and then proves the feasibility of using nonlinear method for flood forecast. This study provides a new method and reference for flood control and water resources management in the local region.

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