Denno, P.
, Dickerson, C.
and Harding, J.
(2017),
Production System Identification with Genetic Programming, 15th International Conference on Manufacturing Research, London, UK, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=923277
(Accessed December 11, 2024)
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
Production System Identification with Genetic Programming
Published
Author(s)
, Charles Dickerson, Jenny Harding
Abstract
Modern system identification methodologies use artificial neural nets, integer linear programming, genetic algorithms, and swarm intelligence to discover system models. Paring genetic programming, a variation of genetic algorithms, with Petri nets seems to offer attractive alternative means to discover system behaviour and structure. Yet to date, very little work has examined this pairing of technologies. Petri nets provide a grey box model of the system useful to verifying system behaviour and interpreting the meaning of operational data. Genetic programming promises a simple yet robust tool to search the space of candidate systems. Genetic programming is inherently highly parallel. This paper describes early experience with genetic programming of Petri nets to discover the best interpretation of operational data. The systems studied are serial production lines with buffers.Conference Dates
September 5-7, 2017
Conference Location
London, UK
Conference Title
15th International Conference on Manufacturing Research
Pub Type
Conferences
Download Paper
Keywords
system identification, genetic programming, Petri nets
Citation
Issues
If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.
Created September 6, 2017, Updated April 12, 2022