scholarly journals Genetic Algorithm Coupled with Neural Networks to Guesstimate the Subsurface Features of the Earth

2020 ◽  
Vol 1 (3) ◽  
pp. 13-27
Author(s):  
A. Stanley Raj ◽  
Y. Srinivas ◽  
R. Damodharan ◽  
B. Chendhoor ◽  
M. Sanjay Vimal
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Soft Computing ◽  
Surface Characteristics ◽  
Advantages And Disadvantages ◽  
Resistivity Method ◽  
The Earth ◽  
Non Linear ◽  
Artificial Neural

Electrical resistivity method is often used to estimate the subsurface structure of the earth. Many inversion algorithms are available to estimate the subsurface features. However, predicting the exact parameter in the non-linear subsurface of the earth is difficult because of its complex composition. Soft computing tools can approximate the subsurface parameters more clearly. Each soft computing tool has certain advantages and disadvantages. A hybrid formation of algorithms will make the decision more appropriate than depending on a single tool. Here in our study the data obtained through Vertical Electrical Sounding has been used to determine the sub surface characteristics of earth viz., true resistivity and thickness. Artificial Neural Networks (ANN) requires certain optimizing procedures. Here in this paper, Genetic Algorithm (GA) is applied to optimize Artificial Neural Networks (ANN). This coupled approach is tested with the field data. Error percentage of algorithm nearly mimics the behavior of earth and is verified. The best performance result shows that this technique can be implemented to estimate the non-linear characteristics of the earth more noticeably.

THE SYSTEM OF AUTOMATED DEVELOPMENT, LEARNING AND EXECUTION OF ARTIFICIAL NEURAL NETWORKS

2019 ◽  
pp. 100-107
Author(s):  
V.A. Sobolevsky
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Automated Generation ◽  
Advantages And Disadvantages ◽  
Automated Learning ◽  
Ice Drift ◽  
Artificial Neural ◽  
The Creation

Goal: the need for systems of automated generation of models of complexly formalized objects is considered. The approach to the creation of such a system based on deep learning is described. Materials and methods: the article describes the architecture of the application of automated learning, based on deep learning, in particular on the basis of the genetic algorithm. Results: the testing of the presented system was carried out on the example of solving the problem of predicting the parameters of ice drift on the Northern Dvina River. Conclusion: the advantages and disadvantages, features of implementation, the scope of the presented system are shown.

scholarly journals Stochastic technique for solutions of non-linear fin equation arising in thermal equilibrium model

2020 ◽  
Vol 24 (5 Part A) ◽  
pp. 3013-3022
Author(s):  
Iftikhar Ahmad ◽  
Hina Qureshi ◽  
Muhammad Bilal ◽  
Muhammad Usman
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Interior Point ◽  
Hybrid Approach ◽  
Pattern Search ◽  
Active Set ◽  
Non Linear ◽  
Artificial Neural ◽  
Interior Point Technique

In this study, a stochastic numerical technique is used to investigate the numerical solution of heat transfer temperature distribution system using feed forward artificial neural networks. Mathematical model of fin equation is formulated with the help of artificial neural networks. The effect of the heat on a rectangular fin with thermal conductivity and temperature dependent internal heat generation is calculated through neural networks optimization with optimizers like active set technique, interior point technique, pattern search, genetic algorithm and a hybrid approach of pattern search - interior point technique, genetic algorithm - active set technique, genetic algorithm - interior point technique, and genetic algorithm - sequential quadratic programming with different selections of weights. The governing fin equation is transformed into an equivalent non-linear second order ODE. For this transformed ODE model we have performed several simulations to provide the justification of better convergence of results. Moreover, the effectiveness of the designed models is validated through a complete statistical analysis. This study reveals the importance of rectangular fins during the heat transformation through the system.

MATHEMATICAL SUPPORT FOR INDUCTION SOLDERING CONTROL BASED ON INTELLIGENT METHODS OF INFORMATION PROCESSING

2021 ◽  
pp. 81-87
Author(s):  
А.В. Милов
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Information Processing ◽  
Mathematical Models ◽  
Technological Process ◽  
Artificial Neural ◽  
Intelligent Methods

В статье представлены математические модели на основе искусственных нейронных сетей, используемые для управления индукционной пайкой. Обучение искусственных нейронных сетей производилось с использованием многокритериального генетического алгоритма FFGA. This article presents mathematical models based on artificial neural networks used to control induction soldering. The artificial neural networks were trained using the FFGA multicriteria genetic algorithm. The developed models allow to control induction soldering under conditions of incomplete or unreliable information, as well as under conditions of complete absence of information about the technological process.

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