THE APPLICATION OF A MODIFIED CONDITIONAL PROBABILITY COMPUTER TO CHEMICAL PROCESS OPTIMIZATION,

Abstract

The application of a conditional probability computer to the steady-state optimization of a simple chemical process has been investigated. On the basis of the work of previous investigators, the memory structure of the conditional probability computer was modified in a way designed to accelerate the learning process and improve the predictive capabilities of the computer. The proposed modifications were studied by optimizing an analog simulation of a simple chemcial process subjected to a variety of uncontrolled disturbances. The investigations served to delineate some of the special problems encountered in the application of a binary pattern recognition device such as the conditional probability computer to process optimization. The proposed modifications were successful in achieving significant acceleration in the learning process and improvement in predictive capabilities over that offered by a conventional conditional probability computer. (Author)

Document Details

Document Type
Technical Report
Publication Date
Jun 03, 1963
Accession Number
AD0617599

Entities

People

  • E. B. Tinker
  • P. N. Nikiforuk

Organizations

  • University of Saskatchewan

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Chemical Engineering
  • Computers
  • Engineering
  • Learning
  • Optimization
  • Pattern Recognition
  • Probability
  • Recognition
  • Simulations
  • Simulators
  • Steady State

Readers

  • Neural Network Machine Learning.
  • Statistical inference.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms