A Hierarchical Algorithm for Neural Training and Control. Revision

Abstract

Lately, there has been an extensive interest in the possible uses of neural networks for nonlinear system identification and control. In this paper, we provide a framework for the simultaneous identification and control of a class of unknown, uncertain nonlinear systems. The identification portion relies on modeling the system by a neural network which is trained via a local variant of the Extended Kalman Filter. We will discuss this local algorithm for training a neural network to approximate a nonlinear feedback system. We also give a dynamic programming-based method of deriving near optimal control inputs for the real plant based on this approximation and a measure of its error (covariance). Finally, we combine these methods in a hierarchical algorithm for identification and control of a class of uncertain, unknown systems. The complexity of the whole algorithm is analyzed.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1992
Accession Number
ADA459605

Entities

People

  • M. M. Livstone
  • M. S. Branicky
  • T. V. Theodosopoulos

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Closed Loop Systems
  • Computational Complexity
  • Computer Programming
  • Computer Science
  • Covariance
  • Dynamic Programming
  • Electrical Engineering
  • Equations Of State
  • Filters
  • Gaussian Noise
  • Iterations
  • Kalman Filters
  • Mathematical Filters
  • Neural Networks
  • Recurrent Neural Networks
  • Training

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks