Local control gradients criterion for selection of neuroemulators for model reference adaptive neurocontrol

  • Authors:
  • A. N. Chernodub

  • Affiliations:
  • Institute of Mathematical Machines and Systems Problems NASU, Kiev, Ukraine

  • Venue:
  • Optical Memory and Neural Networks
  • Year:
  • 2012

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Abstract

We discuss neural identification and control of nonlinear dynamic plant. We analyze a problem of selection of a proper neuroemulator for training neurocontrollers, and we propose a new effective criterion based on the analysis of local gradients of neuroemulator's input neurons. We present results of numerical simulations of neurocontroller training by a gradient descent method and by an Extended Kalman Filter method.