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Advances in Computer Science and Information Engineering pp 211–215Cite as

The Evolution Strategy Implementation of the Least Trimmed Square Algorithm

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Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 169))

Abstract

The least trimmed square algorithm is a robust statistical method. Comparing with the classical least square method, it can eliminate the inference of the abnormal observed data in the process of data analysis. But the least trimmed square algorithm has disadvantages, such as on the condition of nonlinear function fitting. On the other hand, the evolution strategy method has the excellent global searching ability. The paper utilizes the evolution strategy algorithm to realize the solution to least square algorithm, which can overcome the disadvantages of the least square algorithm. Some examples show that the method is feasible.

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References

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Author information

Authors and Affiliations

  1. College of Information & Communication Engineering, Dalian Nationalities University, Dalian, China

    Yuhua Dong & Jifeng Ding

Authors
  1. Yuhua Dong
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  2. Jifeng Ding
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Corresponding author

Correspondence to Yuhua Dong .

Editor information

Editors and Affiliations

  1. Researcher Association, Wuhan Section, International Science & Education, Special No.1, Jiangxia Road of Wuhan, Wuhan, China, People's Republic

    David Jin

  2. Researcher Association, Guangzhou Section, International Science & Education, Jinheng Road, Jinbi Garden 85-1102 144, Guang Zhou, China, People's Republic

    Sally Lin

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© 2012 Springer-Verlag GmbH Berlin Heidelberg

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Cite this paper

Dong, Y., Ding, J. (2012). The Evolution Strategy Implementation of the Least Trimmed Square Algorithm. In: Jin, D., Lin, S. (eds) Advances in Computer Science and Information Engineering. Advances in Intelligent and Soft Computing, vol 169. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30223-7_34

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  • DOI: https://doi.org/10.1007/978-3-642-30223-7_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30222-0

  • Online ISBN: 978-3-642-30223-7

  • eBook Packages: EngineeringEngineering (R0)

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