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An Optimised Hybrid Group Method in Data Handling (GMDH) Network

  • Donald DavendraEmail author
  • Petr Martinek
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 554)

Abstract

A novel modular optimized hydrid Group Method in Data Handling (GMDH) network is proposed in this paper. A standard GMDH network is optimized using the Discrete Differential Evolution (DDE) algorithm for an optimized network structure, and Singular Value Decomposition (SVD) is further used for coefficient calculations of the network. The developed DE-GMDH algorithm is tested for fitness accuracy, memory usage and maximal error on a manufacturing problem.

Keywords

Group Method in Data Handling Differential Evolution Machine learning 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer ScienceCentral Washington UniversityEllensburgUSA
  2. 2.Department of Computer Science, Faculty of Electrical Engineering and Computer ScienceVŠB-Technical University of OstravaOstrava-PorubaCzech Republic

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