A Max-plus, DIOID Based Neural Network for Discrete Event System Modeling


Discrete event system simulation models provide an alternative methodology for analyzing behavior of certain types of systems where we wish to emphasize the reaction of components within the system to events that occur either within the system or in the system environment. The implementation of discrete event system simulations has been primarily algorithmic rather than computational. Specific non-traditional algebraic structures have been used to provide a mathematical foundation for modeling discrete event systems in a more formal and rigorous setting. In this paper we present a tutorial introduction to the use of so called max-plus algebras formulated in a universal algebraic setting for discrete event system simulation. We make the observation that slightly modified versions of standard feed-forward neural networks can be used to implement discrete event system simulations in the max-plus algebraic setting.


Mathematics and Statistics

Second Department

Electrical and Computer Engineering

Keywords and Phrases

artificial intelligence; max-plus algebras; neural networks

Document Type

Article - Conference proceedings

Document Version


File Type





© 2003 American Society of Mechanical Engineers (ASME), All rights reserved.

Publication Date

01 Jan 2003