Volume 1, Issue 3, September 2016, Page: 61-65
Least Absolute Integral Method of Data Fitting Based on Algorithm of Simulated Annealing and Neural Network
Maolin Cheng, School of Mathematics and Physics, Suzhou University of Science and Technology, Suzhou, China
Received: Sep. 5, 2016;       Accepted: Sep. 18, 2016;       Published: Oct. 9, 2016
DOI: 10.11648/j.mcs.20160103.15      View  2397      Downloads  84
Abstract
There are many methods related to data fitting, and each method has its distinctive features. The article discusses the method of data fitting function under integral criterion. Since the estimate fitting parameters are complicated, the article combines algorithm of simulated annealing and neural network algorithm to solve the integral with neural network algorithm and solve the unknown parameters with simulated annealing algorithm. By case analog computation of household per capita consumption expenditure of urban and the rural residents in China, it proves that the combination of simulated annealing algorithm and neural network algorithm has strong reliability and high accuracy in terms of new method for least absolute integral data fitting.
Keywords
Data Fitting, Simulated Annealing, Neural Network, Algorithm, Least Absolute Integral Method
To cite this article
Maolin Cheng, Least Absolute Integral Method of Data Fitting Based on Algorithm of Simulated Annealing and Neural Network, Mathematics and Computer Science. Vol. 1, No. 3, 2016, pp. 61-65. doi: 10.11648/j.mcs.20160103.15
Copyright
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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