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A Study on the Rapid Parameter Estimation and the Grey Prediction in Richards Model

Author

Listed:
  • Wang Xiaoying

    (School of Business and Management Engineering, Xi’an Siyuan University, Xi’an710038, China)

  • Liu Sixia

    (Research Center for Semiotics (CeReS), University of Limoges, Limoges87000, France)

  • Huang Yuan

    (School of Communication and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an710121, China)

Abstract

Richards model is a nonlinear curve with four parameters. Usually, the estimation of parameters in Richard model is complicated; and there is little literature on the gray prediction in Richards model is found. Facing these problems, this paper presents a algorithm consisting of the following steps: First, replacing approximately the original data with an arithmetic sequence to rapidly estimate the four parameters of Richards model; then, using them as the initial values to fit the original data by nonlinear least squares, the optimized parameters of Richards model are obtained. The algorithm along with “Kernel” and “IAGO” principles are used for the prediction of grey Richards model. The results from the experiments show that the above algorithms have good practicability and research value.

Suggested Citation

  • Wang Xiaoying & Liu Sixia & Huang Yuan, 2016. "A Study on the Rapid Parameter Estimation and the Grey Prediction in Richards Model," Journal of Systems Science and Information, De Gruyter, vol. 4(3), pages 223-234, June.
    • Handle: RePEc:bpj:jossai:v:4:y:2016:i:3:p:223-234:n:3
    DOI: 10.21078/JSSI-2016-223-12
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