Abstract
Fitting curve is a critical problem in many testing equipment and detection system. But there was larger relative error in fitting curve when the independent variable was relatively small. In this paper, Fitting curve is formulated to a constrained linear programming. a neural dynamics optimization algorithm is obtained by considering the problem in its dual space, and then the dynamic neural network is designed to solve the optimization problem recurrently. The experimental results show that the polynomial coefficients solved by the method is stable, compared with the least square method, the relative error is obviously reduced; The method is simple and requires less samples. It provides a new simple and accurate method of curve fitting for the quantitative detection.
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Acknowledgments
This work is partially supported by National Natural Foundation Project (61304199), The Ministry of science and technology projects for Hong Kong and Maco (2012DFM30040), Major projects in Fujian Province (2013HZ0002-1, 2014YZ0001).
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Xiong, B., Gan, Z., Zou, F., Gao, Y., Du, M. (2017). Curve Fitting Based on Neural Dynamics Optimization. In: Pan, JS., Lin, JW., Wang, CH., Jiang, X. (eds) Genetic and Evolutionary Computing. ICGEC 2016. Advances in Intelligent Systems and Computing, vol 536. Springer, Cham. https://doi.org/10.1007/978-3-319-48490-7_3
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DOI: https://doi.org/10.1007/978-3-319-48490-7_3
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