Factors Affecting the Stability of Nanoemulsions—Use of Artificial Neural Networks

2009 ◽  
Vol 27 (1) ◽  
pp. 37-45 ◽  
Author(s):  
Amir Amani ◽  
Peter York ◽  
Henry Chrystyn ◽  
Brian J. Clark
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Factors Affecting ◽  
Artificial Neural ◽  
The Stability

scholarly journals The use of classical methods and neural networks in deformation studies of hydrotechnical objects

2020 ◽  
Vol 12 (1) ◽  
pp. 718-725
Author(s):  
Maria Mrówczyńska ◽  
Jacek Sztubecki ◽  
Małgorzata Sztubecka ◽  
Izabela Skrzypczak
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cylinder Surface ◽  
Building Structures ◽  
Steel Cylinder ◽  
Factors Affecting ◽  
Initial Stage ◽  
Artificial Neural ◽  
Set Of Points

Abstract Objects’ measurements often boil down to the determination of changes due to external factors affecting on their structure. The estimation of changes in a tested object, in addition to proper measuring equipment, requires the use of appropriate measuring methods and experimental data result processing methods. This study presents a statement of results of geometrical measurements of a steel cylinder that constitutes the main structural component of the historical weir Czersko Polskie in Bydgoszcz. In the initial stage, the estimation of reliable changes taking place in the cylinder structure involved the selection of measuring points essential for mapping its geometry. Due to the continuous operation of the weir, the points covered only about one-third of the cylinder area. The set of points allowed us to determine the position of the cylinder axis as well as skews and deformations of the cylinder surface. In the next stage, the use of methods based on artificial neural networks allowed us to predict the changes in the tested object. Artificial neural networks have proved to be useful in determining displacements of building structures, particularly hydro-technical objects. The above-mentioned methods supplement classical measurements that create the opportunity for carrying out additional analyses of changes in a spatial position of such structures. The purpose of the tests is to confirm the suitability of artificial neural networks for predicting displacements of building structures, particularly hydro-technical objects.

scholarly journals Approximation of the process of changes in deformation of land surface using artificial neural networks

2018 ◽  
Vol 55 ◽  
pp. 00009
Author(s):  
Maria Mrówczyńska ◽  
Jacek Sztubecki
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Land Surface ◽  
Reference System ◽  
Surface Deformation ◽  
Copper Mining ◽  
Vertical Displacements ◽  
Artificial Neural ◽  
The Stability ◽  
The Impact

Artificial neural networks are an interesting method for modelling phenomena, including spatial phenomena, which are difficult to describe with known mathematical models. The properties of neural networks enable their practical application for solving such problems as: approximation, interpolation, identification and classification of patterns, compression, prediction, etc. The article presents the use of multilayer feedforward artificial neural networks for describing the process of changes in land surface deformation in the area of the Legnica-Głogów Copper Mining Centre, located in the southern part of the Fore Sudetic Monocline. Results provided by geodesic monitoring, which consists of land surveying and interpreting data obtained in this way, are undoubtedly significant in terms of identifying the impact of mining on the land surface the results of measurements carried out by precise levelling in the years 19672014 were used to determine changes in land deformation in the Legnica-Głogów Copper Mining Centre. The concept of a flexible reference system was used to assess the stability of points in the measurement and control network stabilized in order to determine vertical displacements. However, the reference system itself was identified on the basis of the critical value of the increment of the square of the norm of corrections to the observations.

Application of Artificial Neural Networks to Predict Cotton Production: A Case Study in Diyarbakır Province, Turkey

2020 ◽  
Author(s):  
Nazire Mikail ◽  
Mehmet Fırat BARAN
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Plant Production ◽  
Good Prediction ◽  
Ann Model ◽  
Cotton Production ◽  
Factors Affecting ◽  
Artificial Neural Network Method ◽  
Artificial Neural

Abstract Cultivators are always curious about the factors affecting yield in plant production. Determining these factors can provide information about the yield in the future. The reliability of information is dependent on a good prediction model. According to the operating process, artificial neural networks imitate the neural network in humans. The ability to make predictions for the current situation by combining the information people have gained from different experiences is designed in artificial neural networks. Therefore, in complex problems, it gives better results than artificial neural networks.In this study, we used an artificial neural network method to model the production of cotton. From a comprehensive datum collection spanning 73 farms in Diyarbakır, Turkey, the mean cotton production was 559.19 kg da-1. There are four factors that are selected as pivotal inputs into this model. As a result, the ultimate ANN model is able to forshow cotton production, which is built on elements such as farm states (cotton area and irrigation periodicity), machinery usage and fertilizer consumption.At the end of the study, cotton yield was estimated with 84% accuracy.

scholarly journals Building artificial neural networks to predict direction and magnitude of wind, current and wave for sailing vessels

2018 ◽  
Vol 8 (2) ◽  
pp. 55-61
Author(s):  
Timur Inan ◽  
Ahmet Fevzi Baba
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Direction ◽  
Wind Wave ◽  
Current Direction ◽  
Wave Direction ◽  
Factors Affecting ◽  
Historical Information ◽  
Sea Current ◽  
Artificial Neural

Current, wind, wave direction and magnitude are important factors affecting the course of ships. These factors may act positively or negatively depending on the course of a vessel. In both cases, optimization of the route according to these conditions, will improve the factors such as labor, fuel and time. In order to estimate the wind, wave, current direction and magnitude for the region to be navigated, it is necessary to develop a system that can make predictions by using historical information. Our study uses historical information from the E1M3A float, which is a part of the POSEIDON system. With this information being used, artificial neural networks were trained and three separate artificial neural networks were created. Artificial neural networks can predict wind direction and speed, direction and speed of sea current, wave direction and heigth. The esmitations made by this system are only valid for the region where the float is located. For different regions, it is necessary to use artificial neural networks trained using the historical information of those regions. This study is an example for prospective studies.

scholarly journals Investigating the Parameters Affecting the Stability of Superparamagnetic Iron Oxide-Loaded Nanoemulsion Using Artificial Neural Networks

2012 ◽  
Vol 13 (4) ◽  
pp. 1386-1395 ◽  
Author(s):  
Gholamreza Ahmadi Lakalayeh ◽  
Reza Faridi-Majidi ◽  
Reza Saber ◽  
Alireza Partoazar ◽  
Shahram Ejtemaei Mehr ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Iron Oxide ◽  
Superparamagnetic Iron Oxide ◽  
Artificial Neural ◽  
The Stability

scholarly journals Use of artificial neural networks for prognosis of charcoal prices in Minas Gerais

CERNE ◽  
2013 ◽  
Vol 19 (2) ◽  
pp. 281-288 ◽  
Author(s):  
Luiz Moreira Coelho Junior ◽  
José Luiz Pereira de Rezende ◽  
André Luiz França Batista ◽  
Adriano Ribeiro de Mendonça ◽  
Wilian Soares Lacerda
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mean Squared Error ◽  
Energy Resource ◽  
Minas Gerais ◽  
Early 21St Century ◽  
Price Series ◽  
Minas Gerais State ◽  
Artificial Neural ◽  
The Stability

Energy is an important factor of economic growth and is critical to the stability of a nation. Charcoal is a renewable energy resource and is a fundamental input to the development of the Brazilian forest-based industry. The objective of this study is to provide a prognosis of the charcoal price series for the year 2007 by using Artificial Neural Networks. A feedforward multilayer perceptron ANN was used, the results of which are close to reality. The main findings are that: real prices of charcoal dropped between 1975 and 2000 and rose from the early 21st century; the ANN with two hidden layers was the architecture making the best prediction; the most effective learning rate was 0.99 and 600 cycles, representing the most satisfactory and accurate ANN training. Prediction using ANN was found to be more accurate when compared by the mean squared error to other studies modeling charcoal price series in Minas Gerais state.

scholarly journals Stability of Back Propagation Training Algorithm for Neural Networks

2012 ◽  
Vol 9 (4) ◽  
pp. 713-719
Author(s):  
Baghdad Science Journal
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Upper Bound ◽  
Back Propagation ◽  
Learning Rate ◽  
Training Algorithm ◽  
Momentum Coefficient ◽  
Artificial Neural ◽  
Updating Rule ◽  
The Stability

In this paper, we derive and prove the stability bounds of the momentum coefficient µ and the learning rate ? of the back propagation updating rule in Artificial Neural Networks .The theoretical upper bound of learning rate ? is derived and its practical approximation is obtained

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