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Identification of the Inverse Dynamics Model: A Multiple Relevance Vector Machines Approach

  • Chuan Li
  • Xianming Zhang
  • Shilong Wang
  • Yutao Dong
  • Jing Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5326)

Abstract

Relevance vector machines (RVM) is a machine learning approach with good nonlinear approximation capacity and generalization performance. In order to solve the inverse model for nonlinear systems, a multiple relevance vector machines (MRVM) based inverse dynamics model identification approach was presented. The input and output variables were allocated into multiple calculational subspaces according to their differential orders for the system. The RVM was put forward to identify the influence of the outputs to the inputs with a certain differential order in each subspace. Moreover, another RVM was delivered to connect all subspaces, such that the MRVM based inverse dynamics identification model for the nonlinear systems was constructed. At last it was applied to identify the inverse dynamics of a high temperature exchanger for the generator. And the result validates the effectiveness of the proposed approach.

Keywords

Inverse dynamics Relevance vector machines Nonlinear system Sparse Bayesian learning Identification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Chuan Li
    • 1
    • 2
  • Xianming Zhang
    • 1
  • Shilong Wang
    • 2
  • Yutao Dong
    • 1
  • Jing Chen
    • 1
  1. 1.Engineering Research Center for Waste Oil Recovery of Ministry of EducationChongqing Technology and Business UniversityChongqingChina
  2. 2.College of Mechanical EngineeringChongqing UniversityChongqingChina

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