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  2261      Downloads  83
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.
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 © 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.
Zhang X. D, J. N. Yu, L. P. Guo, J. G. Zhang, Q. H. Ding (2011). Prediction of modeling of nonlinear times Series based on Improved BP neural network. Journal of Mianyang Normal University, 30: 84-88.
Gao X. J. (2011). Application of BP neural network in prediction of the liver cirrhosis's cure. Mathematical Theory and Applications, 31: 20-23.
Gavin J. J. B. Bowden, G. C. Nixon, H. R. Dandy, M. H. Maier (2006). Forecasting chlorine residuals in a water distribution system using a general regression neural network. Mathematical and Computer Modelling, 44: 69-484.
Hu J. X, J. Zeng (2010). A Fast learning algorithm of global convergence for BP-neural network. Journal of Systems Science and Mathematical Sciences, 30: 604-610.
Jia H. P. (2012). GRNN neural network in the application of power system load forecasting. Electronic Design Engineering, 20: 14-16.
Alrefaei M. H, A. H. Diabat (2009). A simulated annealing technique for multi-objective simulation optimization. Applied Mathematics and Computation, 215: 3029-3035.
Anagnostopoulos K. P, L. Kotsikas (2010). Experimental evaluation of simulated annealing algorithms for the time-cost trade-off problem. Applied Mathematics and Computation, 217: 260-270.
Chen J, Y. Liu (2011). Traffic prediction research of neural network combined simulated annealing algorithm. Computer Engineering and Design, 32: 2138-2145.
Gao S. (2002). Research on annealing strategy in Simulated Annealing Algorithm. Aeronautical Computer Technique, 32: 20-25.
Gu Y. X, B. W. Xiang, G. Z. Zhao (2005). An improved simulated annealing algorithm for global optimization problems with continuous variables. Systems Engineering Theory & Practice, 25: 103-177.
Shi H., Y. L. Wang, F. L. Weng (2011). Composite prediction model of BP neural networks and fuzzy time series and its application. Journal of Computer Application, 31: 90-92.
Xiao H., S. D. Liu, X. Y. Huang, L. Jin (2010). A study on neural network ensemble forecast model based on kernel principal component analysis. Computer Simulation, 27: 163-167.
Qiu Q. R, T. Yu (2011). BP neural network forecast model based on principal component analysis for the real estate price of prediction. Journal of Hunan University of Arts and Science (Natural Science Edition), 23: 24-27.
Li H. X, C. W. Li (2009). Application of artificial neural network in predicting the number of graduate students. Mathematics in Practice and Theory, 39: 27-33.
Feng Z. Y, M. H. Wang (2011). Essential analysis on the generalization problem of feedforward neural network used in economic forcasting. Journal of Shaanxi University of Science & Technology (Natural Science Edition), 29: 108-111.
Guo D. L, H. M. Xia, Y. Q. Zhou (2010). Hybrid simulation annealing and evolution strategy algorithm in non-linear parameter estimation application. Mathematics in Practice and Theory, 40: 91-98.
Hao Z, F. Hong (2009). Using genetic/simulated annealing algorithm to solve disassembly sequence planning. Journal of Systems Ensnaring and Electronics, 20: 906-912.
Jin L. X. H. W. Tang, B. Li, M. J. Ji, X. Z. Zhu (2005). A simulated annealing algorithm for continuous functions and its convergence properties. Mathematica Numerica Sinica, 27: 19-30.
Liu J. J, M. F. Chen, Z. P. Ye (2010).The estimation of the non-linear model parameter based on combination of damped least-squares method and simulated annealing method. Journal of Jinggangshan University (Natural Science), 31: 10-14.
Michael A. H, M. Brasel, J. Morig, F. Tusch, P. Werner, algorithms for minimizing mean flow time in an open shop. Mathematical and Computer Modelling, 48: 1279-1293.
Browse journals by subject