Multi-innovation generalized extended stochastic gradient algorithm for multi-input multi-output nonlinear Box-Jenkins systems based on the auxiliary model

  • Authors:

  • Jing Chen;Xiuping Wang

  • Affiliations:

  • Control Science and Engineering Research Center, Jiangnan University, Wuxi, PR China;Wuxi Professional College of Science and Technology, Wuxi, PR China

  • Venue:
  • LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part I
  • Year:
  • 2010

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Abstract

An auxiliary model based multi-innovation generalized extended stochastic gradient algorithm is developed for multivariable nonlinear Box-Jenkins systems. The basic idea is to construct an auxiliary model using the measured data and to replace the unknown terms in the information vector with their estimates, i.e., the outputs of the auxiliary model. The proposed algorithm can give high accurate parameter estimation compared with existing stochastic gradient algorithms. A simulation example is given.