Study on the Forecast Model of Water Quantity on the Basis of BP Artificial Neural Network

2013 ◽  
Vol 475-476 ◽  
pp. 188-191
Author(s):  
Xiao Bin Ding
Keyword(s):  
Water Flow ◽  
Yellow River ◽  
Back Propagation ◽  
Forecast Model ◽  
Bp Network ◽  
Bp Artificial Neural Network ◽  
Functional Fitting ◽  
Artificial Neural ◽  
Propagation Network ◽  
Bp Model

Back Propagation network, Widely used in automatic control, image recognition, hydrological forecasting and water quality evaluation, etc., as one of the Artificial Neural Networks, has stronger property of mapping, classification, functional fitting. This article takes the water flow of Lanzhou section of Yellow river as example by use of BP model to predict the water flow. It is well proved that BP network model can reach the purposes of early warning and forecasting.

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

2010 ◽  
Vol 39 ◽  
pp. 555-561 ◽  
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.

Applying Artificial Neural Networks: Part II. Using near Infrared Data to Classify Tobacco Types and Identify Native Grown Tobacco

1997 ◽  
Vol 5 (1) ◽  
pp. 19-25 ◽  
Author(s):  
Maha Hana ◽  
W.F. McClure ◽  
T.B. Whitaker ◽  
M.W. White ◽  
D.R. Bahler
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Near Infrared ◽  
Back Propagation ◽  
Calibration Model ◽  
Linear Network ◽  
Burley Tobacco ◽  
The Usa ◽  
Artificial Neural ◽  
Propagation Network

Classification of flue-cured and Burley tobacco types with artificial neural networks (ANNs) were studied. Burley tobacco was further classified as either grown in the USA or grown outside the USA. The input data were in the form of near infrared (NIR) spectra, each spectrum containing 19 points. The number of flue-cured and Burley samples were 654 and 959, respectively. The number of native and non-native tobacco samples were 266 and 267, respectively. The models selected for this research were a quadratic classifier, a back-propagation network and a linear network. The results of the calibration model and the true performance for classifying tobacco species were (100%, 100%), (99.38%, 99.39%) and (95.19%, 99.26%) for the quadratic classifier, back-propagation network and linear network, respectively. The identification of native tobacco and its true performance were (100%, 100%) using a quadratic classifier, (89.12%, 88.46%) using a back-propagation network and (80.68%, 79.62%) using a linear network.

Non-Math-Modelling Data Analytical Methods Base on Artificial Neural Networks (ANNs) Applied to Optimize Preparation of Norcantharidin-Loaded Chitosan Nanoparticles

2012 ◽  
Vol 443-444 ◽  
pp. 319-324
Author(s):  
Yang Liu ◽  
Wei Zhang ◽  
Xue Nong Zhang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Chitosan Nanoparticles ◽  
Back Propagation ◽  
Multiple Regression Model ◽  
Bp Network ◽  
Object A ◽  
Cross Linkage ◽  
Artificial Neural ◽  
Modelling Data

The artificial neural networks (ANNs) non-modeling methods were selected to optimize the preparation of loading norcantharidin chitosan nanoparticles (NPs) by ionic cross-linkage. A multiple regression model was constructed for fitting several preparation factors and each of the factor level values was arranged in the L9(34) design table and their linear weighted sum of the normalized value was taken as optimized object. A Back-Propagation (BP) network (3×7×2) in ANNs was created and trained for further checking the optimal results and the trained network was applied to simulate the experiment system and screen the optimal conditions. Finally, the best condition was obtained.

Comparison of BP and GRNN Algorithm for Factory Monitoring

2011 ◽  
Vol 52-54 ◽  
pp. 2105-2110 ◽  
Author(s):  
Ing Jiunn Su ◽  
Chia Chih Tsai ◽  
Wen Tsai Sung
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Predictive Ability ◽  
Network Models ◽  
General Regression Neural Network ◽  
Data Sets ◽  
Artificial Neural ◽  
Propagation Network ◽  
General Regression ◽  
Monitoring Area

Artificial neural networks (ANNs) are one of the most recently explored advanced technologies which show promise in the factory monitoring area. This paper focuses on two particular network models, back-propagation network (BPN) and general regression neural network (GRNN). The prediction accuracy of these two models is evaluated using a practical application situation in a monitor factory. GRNN emerged as a variant of the artificial neural network. Its principal advantages are that it can quickly learn and rapidly converge to the optimal regression surface with large number of data sets. According the simulation results we can show that GRNN is an effective way to considerably improve the predictive ability of BPN.

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.

Study on Back-Propagation Neural Networks in Hydrological Forecast

2014 ◽  
Vol 687-691 ◽  
pp. 2153-2156
Author(s):  
Ri Jun Zhang ◽  
Zhong Sheng Li
Keyword(s):  
Neural Networks ◽  
Traditional Method ◽  
Back Propagation ◽  
Projection Pursuit ◽  
Forecasting Model ◽  
Bp Network ◽  
Hydrological Forecasting ◽  
New Methods ◽  
Hydrological Forecast ◽  
Bp Model

The hydrological forecasting model are established respectively by the traditional method and the new methods, BP network and projection pursuit, in order to study the feasibility and practicality. The result shows that the accuracy of the BP model is within 10%, has better forecasting effect and more practical value than the others.

Prediction Model of Silicon Content in Hot Metal Using Optimized BP Network

2014 ◽  
Vol 933 ◽  
pp. 206-211
Author(s):  
Shu Fang Li ◽  
Shi Liang Chu
Keyword(s):  
Adaptive Learning ◽  
Back Propagation ◽  
Optimal Prediction ◽  
Analysis Model ◽  
Hot Metal ◽  
Bp Network ◽  
Network Algorithms ◽  
Blast Furnace Production ◽  
Propagation Network ◽  
Selection Of

In the course of systematic modeling, the artificial neural networks method is studied. In allusion to the defect of grads descension of traditional back propagation network algorithms, some improving measures have been taken to determine the optimal prediction and analysis model. These measures include adaptive learning, additive momentum, reasonable selection of drive function, and using genetic algorithm to optimize the input parameters. And to learn and predict the utilization of blast furnace production data, better application result is acquired.

Sign in / Sign up

Export Citation Format

Share Document