ANN-Based Wheat Chlorophyll Density Estimation Using Canopy Hyperspectral Vegetation Indices

2012 ◽  
Vol 500 ◽  
pp. 243-249
Author(s):  
Da Cheng Wang ◽  
Luo Rui Sen ◽  
Ji Hua Wang ◽  
Cun Jun Li ◽  
Dong Yan Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Statistical Method ◽  
Prediction Models ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Leaf Chlorophyll ◽  
The Neural Network ◽  
Spectral Vegetation Indices ◽  
Artificial Neural Network Ann ◽  
Traditional Statistical Method

Canopy leaf Chlorophyll Density is a key index for evaluating crop potential photosynthetic efficiency and nutritional stress. Leaf Chlorophyll Density estimate using canopy hyperspectral vegetation indices provides a rapid and non-destructive method to evaluate yield predictions. A systematic comparison of two approaches to estimate Chlorophyll Density using 6 spectral vegetation indices (VIs) was presented in this study. In this study, the traditional statistical method based on power regression analyses was compared to the emerging computationally powerful techniques based on artificial neural network (ANN). The regression models of TCARI 、SAVI 、MSAVI and RDVIgreen were found to be more suitable for predicting Chlorophyll Density when only traditional statistical method was used especially TCARI and RDVI. ANN method was more appropriate to develop prediction models. The comparisons between these two methods were based on analysis of the statistic parameters. Results obtained using Root Mean Square Error (RMSE) for ANNs were significantly lower than the traditional method. From this analysis it is concluded that the neural network is more robust to train and estimate crop Chlorophyll Density from remote sensing data.

A Model on the Correlation between Composition and Mechanical Properties of Mg-Al-Zn Alloys by Using Artificial Neural Network

2005 ◽  
Vol 488-489 ◽  
pp. 793-796 ◽  
Author(s):  
Hai Ding Liu ◽  
Ai Tao Tang ◽  
Fu Sheng Pan ◽  
Ru Lin Zuo ◽  
Ling Yun Wang
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Magnesium Alloys ◽  
Tensile Yield Strength ◽  
Data Set ◽  
The Neural Network ◽  
Artificial Neural ◽  
Prediction Of Mechanical Properties ◽  
Artificial Neural Network Ann

A model was developed for the analysis and prediction of correlation between composition and mechanical properties of Mg-Al-Zn (AZ) magnesium alloys by applying artificial neural network (ANN). The input parameters of the neural network (NN) are alloy composition. The outputs of the NN model are important mechanical properties, including ultimate tensile strength, tensile yield strength and elongation. The model is based on multilayer feedforward neural network. The NN was trained with comprehensive data set collected from domestic and foreign literature. A very good performance of the neural network was achieved. The model can be used for the simulation and prediction of mechanical properties of AZ system magnesium alloys as functions of composition.

scholarly journals Stock Market Prediction Using Artificial Neural Networks

2012 ◽  
Vol 6-7 ◽  
pp. 1055-1060 ◽  
Author(s):  
Yang Bing ◽  
Jian Kun Hao ◽  
Si Chang Zhang
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Stock Exchange ◽  
Back Propagation ◽  
Composite Index ◽  
Network Models ◽  
Back Propagation Neural Network ◽  
Neural Network Models ◽  
The Neural Network

In this study we apply back propagation Neural Network models to predict the daily Shanghai Stock Exchange Composite Index. The learning algorithm and gradient search technique are constructed in the models. We evaluate the prediction models and conclude that the Shanghai Stock Exchange Composite Index is predictable in the short term. Empirical study shows that the Neural Network models is successfully applied to predict the daily highest, lowest, and closing value of the Shanghai Stock Exchange Composite Index, but it can not predict the return rate of the Shanghai Stock Exchange Composite Index in short terms.

scholarly journals A Layered Recurrent Neural Network for Imputing Air Pollutants Missing Data and Prediction of NO 2, O 3, PM 10, and PM 2.5

2020 ◽  
Author(s):  
Hamza Turabieh ◽  
Alaa Sheta ◽  
Malik Braik ◽  
Elvira Kovač-Andrić
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Air Quality ◽  
Recurrent Neural Network ◽  
Missing Values ◽  
Prediction Models ◽  
Air Pollutant ◽  
Forecasting Models ◽  
Monitoring Strategies ◽  
Artificial Neural Network Ann

To fulfill the national air quality standards, many countries have created emissions monitoring strategies on air quality. Nowadays, policymakers and air quality executives depend on scientific computation and prediction models to monitor that cause air pollution, especially in industrial cities. Air pollution is considered one of the primary problems that could cause many human health problems such as asthma, damage to lungs, and even death. In this study, we present investigated development forecasting models for air pollutant attributes including Particulate Matters (PM2.5, PM10), ground-level Ozone (O3), and Nitrogen Oxides (NO2). The dataset used was collected from Dubrovnik city, which is located in the east of Croatia. The collected data has missing values. Therefore, we suggested the use of a Layered Recurrent Neural Network (L-RNN) to impute the missing value(s) of air pollutant attributes then build forecasting models. We adopted four regression models to forecast air pollutant attributes, which are: Multiple Linear Regression (MLR), Decision Tree Regression (DTR), Artificial Neural Network (ANN) and L-RNN. The obtained results show that the proposed method enhances the overall performance of other forecasting models.

scholarly journals Neural Networks Applied to Estimating Subglacial Topography and Glacier Volume

2009 ◽  
Vol 22 (8) ◽  
pp. 2146-2160 ◽  
Author(s):  
Garry K. C. Clarke ◽  
Etienne Berthier ◽  
Christian G. Schoof ◽  
Alexander H. Jarosch
Keyword(s):  
Neural Network ◽  
Regional Scale ◽  
Ice Thickness ◽  
Ice Dynamics ◽  
Ice Caps ◽  
Mountain Glaciers ◽  
The Neural Network ◽  
Glacier Ice ◽  
Subglacial Topography ◽  
Artificial Neural Network Ann

Abstract To predict the rate and consequences of shrinkage of the earth’s mountain glaciers and ice caps, it is necessary to have improved regional-scale models of mountain glaciation and better knowledge of the subglacial topography upon which these models must operate. The problem of estimating glacier ice thickness is addressed by developing an artificial neural network (ANN) approach that uses calculations performed on a digital elevation model (DEM) and on a mask of the present-day ice cover. Because suitable data from real glaciers are lacking, the ANN is trained by substituting the known topography of ice-denuded regions adjacent to the ice-covered regions of interest, and this known topography is hidden by imagining it to be ice-covered. For this training it is assumed that the topography is flooded to various levels by horizontal lake-like glaciers. The validity of this assumption and the estimation skill of the trained ANN is tested by predicting ice thickness for four 50 km × 50 km regions that are currently ice free but that have been partially glaciated using a numerical ice dynamics model. In this manner, predictions of ice thickness based on the neural network can be compared to the modeled ice thickness and the performance of the neural network can be evaluated and improved. From the results, thus far, it is found that ANN depth estimates can yield plausible subglacial topography with a representative rms elevation error of ±70 m and remarkably good estimates of ice volume.

Artificial Neural Networks and Learning Techniques

2016 ◽  
pp. 89-112
Author(s):  
Pushpendu Kar ◽  
Anusua Das
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Research Work ◽  
The Neural Network ◽  
Learning Techniques ◽  
Learning Procedure ◽  
Processing Component ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The recent craze for artificial neural networks has spread its roots towards the development of neuroscience, pattern recognition, machine learning and artificial intelligence. The theoretical neuroscience is basically converging towards the basic concept that the brain acts as a complex and decentralized computer which can perform rigorous calculations in a different approach compared to the conventional digital computers. The motivation behind the study of neural networks is due to their similarity in the structure of the human central nervous system. The elementary processing component of an Artificial Neural Network (ANN) is called as ‘Neuron'. A large number of neurons interconnected with each other mimic the biological neural network and form an ANN. Learning is an inevitable process that can be used to train an ANN. We can only transfer knowledge to the neural network by the learning procedure. This chapter presents the detailed concepts of artificial neural networks in addition to some significant aspects on the present research work.

Prediction of Bending Beam Rheometer Test Outputs Using Artificial Neural Networks

2019 ◽  
Vol 821 ◽  
pp. 500-505
Author(s):  
Mohammad Fuad Aljarrah ◽  
Mohammad Ali Khasawneh ◽  
Aslam Ali Al-Omari ◽  
Mohammad Emad Alshorman
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Models ◽  
Time Interval ◽  
Testing Time ◽  
Bending Beam Rheometer ◽  
The Neural Network ◽  
Creep Stiffness ◽  
Artificial Neural

The major objective of this study is to investigate the possibility of using Artificial Neural Networks in creating prediction models capable of estimating Bending Beam Rheometer outputs; namely creep stiffness, and m-value based on test temperature, modifier content; in our case waste vegetable oil, and testing time interval. A feedforward backpropagation neural network with Bayesian Regulation training algorithm and an SSE performance function was implemented. It was found that the neural network model shows high predictive powers with training and testing performance of 99.8% and 99.2% respectively. Plots between laboratory obtained values and neural network predicted outputs were also considered, and a strong correlation between the two methods was concluded. Therefore, it was reasonable to state that using neural networks to build prediction models in order to find BBR test values is justified.

Research of Intelligent Substation Condition Monitoring Based on the Neural Network

2013 ◽  
Vol 325-326 ◽  
pp. 692-696
Author(s):  
Da Peng Chai ◽  
Qiang Qiang Xue ◽  
Ling Mei Wang ◽  
Xing Yong Zhao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Diagnosis ◽  
Electric Power ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Power Equipment ◽  
The Neural Network ◽  
Condition Monitoring System ◽  
Artificial Neural Network Ann

The substation electric power equipment condition monitoring is the basis of intelligent substation. This paper analyzes the composition of the substation electric power equipment condition monitoring system and monitoring parameters, and with the transformer condition monitoring as an example, this paper proposes fault diagnosis methods of electric power equipment using artificial neural network(ANN).

scholarly journals Analysing 21cm signal with artificial neural network

2017 ◽  
Vol 12 (S333) ◽  
pp. 39-42
Author(s):  
Hayato Shimabukuro ◽  
Benoit Semelin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Spectrum ◽  
Machine Learning Techniques ◽  
Inversion Method ◽  
Observation Data ◽  
The Neural Network ◽  
Learning Techniques ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

AbstractThe 21cm signal at epoch of reionization (EoR) should be observed within next decade. We expect that cosmic 21cm signal at the EoR provides us both cosmological and astrophysical information. In order to extract fruitful information from observation data, we need to develop inversion method. For such a method, we introduce artificial neural network (ANN) which is one of the machine learning techniques. We apply the ANN to inversion problem to constrain astrophysical parameters from 21cm power spectrum. We train the architecture of the neural network with 70 training datasets and apply it to 54 test datasets with different value of parameters. We find that the quality of the parameter reconstruction depends on the sensitivity of the power spectrum to the different parameter sets at a given redshift and also find that the accuracy of reconstruction is improved by increasing the number of given redshifts. We conclude that the ANN is viable inversion method whose main strength is that they require a sparse extrapolation of the parameter space and thus should be usable with full simulation.

Prediction of Detonation Pressure of Aluminized Explosive by Artificial Neural Network

2013 ◽  
Vol 641-642 ◽  
pp. 460-463
Author(s):  
Yong Gang Liu ◽  
Xin Tian ◽  
Yue Qiang Jiang ◽  
Gong Bing Li ◽  
Yi Zhou Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimal Condition ◽  
Detonation Pressure ◽  
Ann Model ◽  
Training Set ◽  
The Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Good Agreement

In this study, a three-layer artificial neural network(ANN) model was constructed to predict the detonation pressure of aluminized explosive. Elemental composition and loading density were employed as input descriptors and detonation pressure was used as output. The dataset of 41 aluminized explosives was randomly divided into a training set (30) and a prediction set (11). After optimized by adjusting various parameters, the optimal condition of the neural network was obtained. Simulated with the final optimum neural network [6–9–1], calculated detonation pressures show good agreement with experimental results. It is shown here that ANN is able to produce accurate predictions of the detonation pressure of aluminized explosive.

The Neural Network Prediction to the Wear and Tear Fault of Thrust Bearing in Air Compressor

2013 ◽  
Vol 333-335 ◽  
pp. 1758-1761
Author(s):  
Song He Zhang ◽  
Yue Gang Luo ◽  
Bin Wu ◽  
Bing Cheng Wang
Keyword(s):  
Neural Network ◽  
Traditional Method ◽  
Thrust Bearing ◽  
Axial Thrust ◽  
Air Compressor ◽  
The Neural Network ◽  
Wear And Tear ◽  
Neural Network Prediction ◽  
Tear Fault ◽  
Artificial Neural Network Ann

The wear and tear allowances (displacements) of axial thrust bearing in air compressor was diagnosed and predicted, applying the model of artificial neural network (ANN), and compared with the traditional method of diagnosis and prediction. It showed that the results of diagnosis and prediction are more precise than that of traditional method. It can diagnosis and predicts the wear and tear allowances of axial thrust bearing better.

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