scholarly journals An Application of Neural Network Technique to Correct the Dome Temperature Effects on Pyrgeometer Measurements

2006 ◽  
Vol 23 (1) ◽  
pp. 80-89 ◽  
Author(s):  
Amauri P. Oliveira ◽  
Jacyra Soares ◽  
Marija Z. Božnar ◽  
Primož Mlakar ◽  
João F. Escobedo
Keyword(s):  
Neural Network ◽  
Temperature Effects ◽  
Longwave Radiation ◽  
Global Solar Radiation ◽  
Time Of Day ◽  
Network Training ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Using Data ◽  
The City

Abstract This work describes an application of a multilayer perceptron neural network technique to correct dome emission effects on longwave atmospheric radiation measurements carried out using an Eppley Precision Infrared Radiometer (PIR) pyrgeometer. It is shown that approximately 7-month-long measurements of dome and case temperatures and meteorological variables available in regular surface stations (global solar radiation, air temperature, and air relative humidity) are enough to train the neural network algorithm and correct the observed longwave radiation for dome temperature effects in surface stations with climates similar to that of the city of São Paulo, Brazil. The network was trained using data from 15 October 2003 to 7 January 2004 and verified using data, not present during the network-training period, from 8 January to 30 April 2004. The longwave radiation values generated by the neural network technique were very similar to the values obtained by Fairall et al., assumed here as the reference approach to correct dome emission effects in PIR pyrgeometers. Compared to the empirical approach the neural network technique is less limited to sensor type and time of day (allows nighttime corrections).

scholarly journals IMPLEMENTASI METODE KOHONEN UNTUK PREDIKSI CURAH HUJAN (STUDI KASUS : KOTA PONTIANAK)

2017 ◽  
Vol 4 (2) ◽  
pp. 198
Author(s):  
Fatma Agus Setyanngsih
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Self Organizing Map ◽  
Rainfall Prediction ◽  
The Neural Network ◽  
Network Time ◽  
Neural Network Algorithm ◽  
Network Prediction ◽  
The City ◽  
Self Organizing

<p><em>The prediction to determine the rainfall in Pontianak is much needed. One of them is using a neural network algorithm using SOM (Self Organizing Maping) with the data used in January 2010-2013. The purpose of this study was to determine the rainfall prediction in the city of Pontianak with parameters of air temperature, relative humidity, air pressure and wind speed. The results showed that the value of MSE is obtained when studying the data network prediction in January of 2010 until 2013 using the Neural Network-SOM learning process with the amount of 1 neuron and using 124 datas, with MSE value 0,0148.</em><strong> </strong></p><p><strong><em>Keywords</em></strong><em>: </em><em>Rainfall, Neural Network, Time Series, Self Organizing Map</em></p><p><em>Prediksi untuk mengetahui curah hujan yang terjadi di Pontianak sangat dibutuhkan salah satunya yaitu menggunakan algoritma jaringan syaraf tiruan dengan pengelompokkannya menggunakan SOM (Self Organizing Map) dengan data yang digunakan adalah data di bulan januari tahun 2010-2013. Tujuan dari penelitian ini adalah untuk mengetahui prediksi curah hujan di kota Pontianak dengan parameter suhu udara, kelembababn relative, tekanan udara dan kecepatan angin. Hasil penelitian menunjukkan bahwa nilai MSE ini didapatkan saat jaringan mempelajari data prediksi pada bulan januari di tahun 2010 sampai tahun 2013 dengan menggunakan proses pembelajaran JST SOM dengan jumlah neuron 1 dan menggunakan 124 data, dengan nilai MSE 0,0148. </em></p><p><em></em><em><strong><em>Kata kunci</em></strong><strong><em>:</em></strong><em> </em><em>Curah Hujan, Jaringan Syaraf Tiruan, Time Series, Self Organizing Map</em></em></p>

scholarly journals Neural Network Model for Mathematic Scores Prediction: Case Study in SMK Negeri Pakis Aji, Jepara, Indonesia

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Adi Sucipto ◽  
Joko Minardi
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Mean Square ◽  
Mathematics Scores ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Conducting Research ◽  
Using Data

Aim of this research is to apply Neural Network Algorithm to predict score of mathematic in the national exam. During the time, the teacher only provided national exam materials and additional tryout tests without knowing how to predict the exam scores in mathematics subject. Data mining neural network algorithm obtained \Root Mean Square Error (RMSE) values which were used as basic improvement and clustering class By conducting research using data mining neural network algorithm, it proved that this model can be used to predict scores of Mathematics subject at SMK Negeri 1 Pakis Aji.. The result of this research by using data mining neural network algorithm found RMSE 0138 +/- 0.092. The lower the RMSE values the more accurate the neural network to predict mathematics scores of SMK Negeri 1 Pakis Aji.Received: 18 Agustus 2019; Accepted: 5 Januari 2020; Published: 14 January 2020

scholarly journals ELEMENTARY PERCEPTRON AS A TOOL FOR THE TRANSIENTS ANALYZING

2020 ◽  
pp. 84-93
Author(s):  
Maksim I. Koshcheev ◽  
Alexandr L. Slavutskiy ◽  
Leonid A. Slavutskii
Keyword(s):  
Neural Network ◽  
Transition Process ◽  
Time Interval ◽  
Electrical Networks ◽  
Network Training ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Short Circuits ◽  
Aperiodic Component

The use of elemental perceptron as the simplest artificial feedforward neural network is proposed to evaluate transient processes in electrical networks. Signals with random amplitude, phase, frequency and attenuation were used to test the neural network algorithm as well as the superposition of an aperiodic component, also having a random amplitude and a time-constant. Each signal from the sample was thus determined by six independent random parameters, varying in different ranges. Based on the results of numerical modeling it is shown that such signals are typical for oscillograms of current at short circuits on power lines. It is shown that at the frequency of digitization of signals of 600 Hz in measuring organs on the time interval during industrial frequency it is possible to assess the parameters of a transition process with the accuracy not lower than several percents. The accuracy of the definition for each parameter depending on the range of their variation is analyzed. Transition process parameters that have the greatest impact on neural network training and testing errors are highlighted. Estimates of the possible running speed of the proposed neural network algorithm are made.

Self-validating deep learning of continental hydrology through satellite gravimetry and altimetry

2021 ◽  
Author(s):  
Christopher Irrgang ◽  
Jan Saynisch-Wagner ◽  
Robert Dill ◽  
Eva Boergens ◽  
Maik Thomas
Keyword(s):  
Neural Network ◽  
Satellite Altimetry ◽  
Water Cycle ◽  
Global Water Cycle ◽  
Network Training ◽  
The Neural Network ◽  
Continental Hydrology ◽  
Terrestrial Water ◽  
Combine Machine ◽  
Groundwater Basins

&lt;p&gt;Space-borne observations of terrestrial water storage (TWS) are an essential ingredient for understanding the Earth's global water cycle, its susceptibility to climate change, and for risk assessments of ecosystems, agriculture, and water management. However, the complex distribution of water masses in rivers, lakes, or groundwater basins remains elusive in coarse-resolution gravimetry observations. We combine machine learning, numerical modeling, and satellite altimetry to build and train a downscaling neural network that recovers simulated TWS from synthetic space-borne gravity observations. The neural network is designed to adapt and validate its training progress by considering independent satellite altimetry records. We show that the neural network can accurately derive TWS anomalies in 2019 after being trained over the years 2003 to 2018. Specifically for validated regions in the Amazonas, we highlight that the neural network can outperform the numerical hydrology model used in the network training.&lt;/p&gt;&lt;p&gt;https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL089258&lt;/p&gt;

Research on the Optimized Algorithms on Neural Network

2012 ◽  
Vol 605-607 ◽  
pp. 2175-2178
Author(s):  
Xiao Qin Wu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Simulated Annealing Algorithm ◽  
Fitness Function ◽  
Strong Stability ◽  
Training Algorithm ◽  
Network Training ◽  
The Neural Network ◽  
Annealing Algorithm ◽  
Proper Operation

In order to overcome the disadvantage of neural networks that their structure and parameters were decided stochastically or by one’s experience, an improved BP neural network training algorithm based on genetic algorithm was proposed.In this paper,genetic algorithms and simulated annealing algorithm that optimizes neural network is proposed which is used to scale the fitness function and select the proper operation according to the expected value in the course of optimization,and the weights and thresholds of the neural network is optimized. This method is applied to the stock prediction system.The experimental results show that the proposed approach have high accuracy,strong stability and improved confidence.

Mechanism of neural network training for forecasting the meteorological situation when using GIS

2021 ◽  
pp. 22-29
Author(s):  
M.R. Vagizov ◽  
◽  
E.P. Istomin ◽  
O.N. Kolbina ◽  
A.S. Kochnev ◽  
...  
Keyword(s):  
Neural Network ◽  
Estimation Method ◽  
Stochastic Method ◽  
Neural Network Training ◽  
Forecast Method ◽  
Network Training ◽  
Meteorological Situation ◽  
Previous Sample ◽  
Data Processor ◽  
Using Data

This article is devoted to the mechanisms of neural network training for forecasting the meteorological situation when using GIS. The structural scheme of the GIS under consideration is proposed as a project solution and the main elements allowing to implement neural networks and their training are defined. The stochastic method is chosen as a tool for neural network training as it suggests the most probable outcome of the event based on the previous sample. The article gives an example of testing neural network training as an application program «Data Processor». The results described in the article allow us to judge about the applicability of the selected neural network training method for forecasting meteorological conditions and using data in geoinformation decision-making systems. Keywords: geoinformation system, synoptic forecast method, hydrodynamic forecast method, aggregator, data processor, knowledge base, deterministic method, expert estimation method, stochastic method, neural network, sampling, probability dispersion.

Neural Network-Based Stock Market Return Forecasting Using Data Mining for Variable Reduction

2008 ◽  
pp. 2476-2493 ◽  
Author(s):  
David Encke
Keyword(s):  
Neural Network ◽  
Information Gain ◽  
Network Models ◽  
Linear Regression Models ◽  
Market Returns ◽  
Neural Network Models ◽  
The Neural Network ◽  
Variable Reduction ◽  
Input Variables ◽  
Using Data

Researchers have known for some time that nonlinearity exists in the financial markets and that neural networks can be used to forecast market returns. Unfortunately, many of these studies fail to consider alternative forecasting techniques, or the relevance of the input variables. The following research utilizes an information-gain technique from machine learning to evaluate the predictive relationships of numerous financial and economic input variables. Neural network models for level estimation and classification are then examined for their ability to provide an effective forecast of future values. A cross-validation technique is also employed to improve the generalization ability of the models. The results show that the classification models generate higher accuracy in forecasting ability than the buy-and-hold strategy, as well as those guided by the level-estimation-based forecasts of the neural network and benchmark linear regression models.

scholarly journals Predicting Wins, Losses and Attributes’ Sensitivities in the Soccer World Cup 2018 Using Neural Network Analysis

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3213 ◽  
Author(s):  
Amr Hassan ◽  
Abdel-Rahman Akl ◽  
Ibrahim Hassan ◽  
Caroline Sunderland
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Average Distance ◽  
Deep Understanding ◽  
The Neural Network ◽  
Testing Error ◽  
Training Error ◽  
Using Data ◽  
The Relationship

Predicting the results of soccer competitions and the contributions of match attributes, in particular, has gained popularity in recent years. Big data processing obtained from different sensors, cameras and analysis systems needs modern tools that can provide a deep understanding of the relationship between this huge amount of data produced by sensors and cameras, both linear and non-linear data. Using data mining tools does not appear sufficient to provide a deep understanding of the relationship between the match attributes and results and how to predict or optimize the results based upon performance variables. This study aimed to suggest a different approach to predict wins, losses and attributes’ sensitivities which enables the prediction of match results based on the most sensitive attributes that affect it as a second step. A radial basis function neural network model has successfully weighted the effectiveness of all match attributes and classified the team results into the target groups as a win or loss. The neural network model’s output demonstrated a correct percentage of win and loss of 83.3% and 72.7% respectively, with a low Root Mean Square training error of 2.9% and testing error of 0.37%. Out of 75 match attributes, 19 were identified as powerful predictors of success. The most powerful respectively were: the Total Team Medium Pass Attempted (MBA) 100%; the Distance Covered Team Average in zone 3 (15–20 km/h; Zone3_TA) 99%; the Team Average ball delivery into the attacking third of the field (TA_DAT) 80.9%; the Total Team Covered Distance without Ball Possession (Not in_Poss_TT) 76.8%; and the Average Distance Covered by Team (Game TA) 75.1%. Therefore, the novel radial based function neural network model can be employed by sports scientists to adapt training, tactics and opposition analysis to improve performance.

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