AN APPROACH TO INVERSE MODELING THROUGH THE INTEGRATION OF ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHMS
A hybrid model integrating predictive capabilities of Artificial Neural Network (ANN) and optimization feature of Genetic Algorithm (GA) is developed for the purpose of inverse modeling. The proposed approach is applied to Superplastic forming of materials to predict the material properties which ch...
Main Author: | |
---|---|
Format: | Others |
Published: |
UKnowledge
2007
|
Subjects: | |
Online Access: | http://uknowledge.uky.edu/gradschool_theses/493 http://uknowledge.uky.edu/cgi/viewcontent.cgi?article=1496&context=gradschool_theses |
id |
ndltd-uky.edu-oai-uknowledge.uky.edu-gradschool_theses-1496 |
---|---|
record_format |
oai_dc |
spelling |
ndltd-uky.edu-oai-uknowledge.uky.edu-gradschool_theses-14962015-04-11T05:05:35Z AN APPROACH TO INVERSE MODELING THROUGH THE INTEGRATION OF ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHMS Bedida, Kirthi A hybrid model integrating predictive capabilities of Artificial Neural Network (ANN) and optimization feature of Genetic Algorithm (GA) is developed for the purpose of inverse modeling. The proposed approach is applied to Superplastic forming of materials to predict the material properties which characterize the performance of a material. The study is carried out on two problems. For the first problem, ANN is trained to predict the strain rate sensitivity index m given the temperature and the strain rate. The performance of different gradient search methods used in training the ANN model is demonstrated. Similar approach is used for the second problem. The objective of which is to predict the input parameters, i.e. strain rate and temperature corresponding to a given flow stress value. An attempt to address one of the major drawbacks of ANN, which is the black box behavior of the model, is made by collecting information about the weights and biases used in training and formulating a mathematical expression. The results from the two problems are compared to the experimental data and validated. The results indicated proximity to the experimental data. 2007-01-01T08:00:00Z text application/pdf http://uknowledge.uky.edu/gradschool_theses/493 http://uknowledge.uky.edu/cgi/viewcontent.cgi?article=1496&context=gradschool_theses University of Kentucky Master's Theses UKnowledge Artificial Neural Networks|Genetic Algorithms|Hybrid Modeling|Gradient Search|Superplastic Forming |
collection |
NDLTD |
format |
Others
|
sources |
NDLTD |
topic |
Artificial Neural Networks|Genetic Algorithms|Hybrid Modeling|Gradient Search|Superplastic Forming |
spellingShingle |
Artificial Neural Networks|Genetic Algorithms|Hybrid Modeling|Gradient Search|Superplastic Forming Bedida, Kirthi AN APPROACH TO INVERSE MODELING THROUGH THE INTEGRATION OF ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHMS |
description |
A hybrid model integrating predictive capabilities of Artificial Neural Network (ANN) and optimization feature of Genetic Algorithm (GA) is developed for the purpose of inverse modeling. The proposed approach is applied to Superplastic forming of materials to predict the material properties which characterize the performance of a material. The study is carried out on two problems. For the first problem, ANN is trained to predict the strain rate sensitivity index m given the temperature and the strain rate. The performance of different gradient search methods used in training the ANN model is demonstrated. Similar approach is used for the second problem. The objective of which is to predict the input parameters, i.e. strain rate and temperature corresponding to a given flow stress value. An attempt to address one of the major drawbacks of ANN, which is the black box behavior of the model, is made by collecting information about the weights and biases used in training and formulating a mathematical expression. The results from the two problems are compared to the experimental data and validated. The results indicated proximity to the experimental data. |
author |
Bedida, Kirthi |
author_facet |
Bedida, Kirthi |
author_sort |
Bedida, Kirthi |
title |
AN APPROACH TO INVERSE MODELING THROUGH THE INTEGRATION OF ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHMS |
title_short |
AN APPROACH TO INVERSE MODELING THROUGH THE INTEGRATION OF ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHMS |
title_full |
AN APPROACH TO INVERSE MODELING THROUGH THE INTEGRATION OF ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHMS |
title_fullStr |
AN APPROACH TO INVERSE MODELING THROUGH THE INTEGRATION OF ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHMS |
title_full_unstemmed |
AN APPROACH TO INVERSE MODELING THROUGH THE INTEGRATION OF ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHMS |
title_sort |
approach to inverse modeling through the integration of artificial neural networks and genetic algorithms |
publisher |
UKnowledge |
publishDate |
2007 |
url |
http://uknowledge.uky.edu/gradschool_theses/493 http://uknowledge.uky.edu/cgi/viewcontent.cgi?article=1496&context=gradschool_theses |
work_keys_str_mv |
AT bedidakirthi anapproachtoinversemodelingthroughtheintegrationofartificialneuralnetworksandgeneticalgorithms AT bedidakirthi approachtoinversemodelingthroughtheintegrationofartificialneuralnetworksandgeneticalgorithms |
_version_ |
1716801226241212416 |