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Title: Adaptive model predictive process control using neural networks

Patent ·
OSTI ID:871108
 [1];  [2];  [3]
  1. Los Alamos, NM
  2. Mazomanie, WI
  3. Espanola, NM
+ Show Author Affiliations

A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data.

Research Organization:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
DOE Contract Number:
W-7405-ENG-36
Assignee:
Regents of University of California Office of Technology Transfer (Alemeda, CA)
Patent Number(s):
US 5659667
OSTI ID:
871108
Country of Publication:
United States
Language:
English

References (3)

Temporal difference method for multi-step prediction: application to power load forecasting conference August 2002
The Computational Brain book January 1992
Optimization and control of a small-angle negative ion source using an on-line adaptive controller based on the connectionist normalized local spline neural network journal November 1992

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