Controlling Industrial Processes Through Supervised, Feedforward Neural Networks

Author

Alice E. Smith
Cihan H. Dagli, Missouri University of Science and TechnologyFollow

Abstract

Multi-layer, feedforward perceptron neural networks produce hetero- and auto- pattern associators which can be applied to a wide range of problems. The area of process monitoring and control is one of numerous inputs and outputs, which are normally non-determinative and do not adhere to known probability distributions. By training neural networks through supervised learning, such as backpropagation, a mechanized tool can be created which offers advantages over traditional methods based on statistics. Significant benefits are the ability to discern complex relationships and trends rather than assuming distributions (usually Gaussian) or specifying algorithms, the ability to integrate in real time large amounts of continuous data and adapt incrementally to changes in process, and the ability to handle noisy or incomplete data. © 1991.

Recommended Citation

A. E. Smith and C. H. Dagli, "Controlling Industrial Processes Through Supervised, Feedforward Neural Networks," Computers and Industrial Engineering, vol. 21, no. 1 thru 4, pp. 247 - 251, Elsevier, Jan 1991.

The definitive version is available at https://doi.org/10.1016/0360-8352(91)90096-O

Department(s)

Engineering Management and Systems Engineering

International Standard Serial Number (ISSN)

0360-8352

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2023 Elsevier, All rights reserved.

Publication Date

01 Jan 1991