Year: 1994
Type: dissertation
Abstract: The treatment of manufacturing problems, whether in process control, process optimization, or system design and planning, can be helped by input-output models, namely, relationships between input and output variables.Artificial neural networks present an opportunity to "learn" empirically established relationships and apply them subsequently in order to solve a particular problem.In light of the increasing amount of applications of neural networks, the objective of this thesis is to evaluate the ability of neural networks to generate accurate models for manufacturing applications.Various neural network models has been tested on a number of "test bed" problems which represent the problems typically encountered in manufacturing processes and systems to assess the reliability of neural network models and to determine the efficacy of their modeling capabilities.
Author: Alvin L. Ramsey
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Johns Hopkins University
OpenAlex ID: W1546710968