Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
Neural Networks and Rules-based Systems used to Find Rational and
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The paper presents a method to solve singular non-linear system representing polytrophic and isothermal sphere using neural network optimized by evolutionary computational approach. A trial solution of the system is written as a feed-forward neural network containing adaptive parameters (weights and biases). We prepare a fitness evaluation function defining unsupervised error. The optimization of the error defines the accuracy in the model that is highly stochastic in nature. Genetic algorithm is exploited as a tool for global convergence and active set algorithm as a rapid local search. The given scheme is tested on the model with polytrophic index 5=λ . A comparative study is made with exact and optimal Homtopy asymptotic method. The stability and reliability of the proposed scheme is investigated by a comprehensive statistical analysis. The proposed results are found to be in good agreement with exact solution as well as numerical solvers.
Junaid Ali Khan. 2012. \u201cAn Application of Evolutionary Computational Technique to Non-Linear Singular System arising in Polytrophic and Isothermal sphere\u201d. Global Journal of Research in Engineering - I: Numerical Methods GJRE-I Volume 12 (GJRE Volume 12 Issue I1): .
Crossref Journal DOI 10.17406/gjre
Print ISSN 0975-5861
e-ISSN 2249-4596
The methods for personal identification and authentication are no exception.
The methods for personal identification and authentication are no exception.
Total Score: 103
Country: Pakistan
Subject: Global Journal of Research in Engineering - I: Numerical Methods
Authors: Junaid Ali Khan, Muhammad Asif Zahoor Raja, Ijaz Mansoor Qureshi (PhD/Dr. count: 0)
View Count (all-time): 203
Total Views (Real + Logic): 5428
Total Downloads (simulated): 2703
Publish Date: 2012 03, Wed
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The paper presents a method to solve singular non-linear system representing polytrophic and isothermal sphere using neural network optimized by evolutionary computational approach. A trial solution of the system is written as a feed-forward neural network containing adaptive parameters (weights and biases). We prepare a fitness evaluation function defining unsupervised error. The optimization of the error defines the accuracy in the model that is highly stochastic in nature. Genetic algorithm is exploited as a tool for global convergence and active set algorithm as a rapid local search. The given scheme is tested on the model with polytrophic index 5=λ . A comparative study is made with exact and optimal Homtopy asymptotic method. The stability and reliability of the proposed scheme is investigated by a comprehensive statistical analysis. The proposed results are found to be in good agreement with exact solution as well as numerical solvers.
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