An Enhanced Petri Net Model to Verify and Validate a Neural-Symbolic Hybrid System

An Enhanced Petri Net Model to Verify and Validate a Neural-Symbolic Hybrid System

Ricardo R. Jorge (National Centre of Investigation and Technological Development, Mexico), Gerardo R. Salgado (National Centre of Investigation and Technological Development, Mexico) and Vianey G.C. Sánchez (National Centre of Investigation and Technological Development, Mexico)
DOI: 10.4018/jssci.2009070103
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

As the Neural-Symbolic Hybrid Systems (NSHS) gain acceptance, it increases the necessity to guarantee the automatic validation and verification of the knowledge contained in them. In the past, such processes were made manually. In this article, an enhanced Petri net model is presented to the detection and elimination of structural anomalies in the knowledge base of the NSHS. In addition, a reachability model is proposed to evaluate the obtained results of the system versus the expected results by the user. The validation and verification method is divided in two stages: 1) it consists of three phases: rule normalization, rule modeling and rule verification. 2) It consists of three phases: rule modeling, dynamic modeling and evaluation of results. Such method is useful to ensure that the results of a NSHS are correct. Examples are presented to demonstrate the effectiveness of the results obtained with the method.

Complete Article List

Search this Journal:
Reset
Open Access Articles: Forthcoming
Volume 9: 4 Issues (2017): 3 Released, 1 Forthcoming
Volume 8: 4 Issues (2016)
Volume 7: 4 Issues (2015)
Volume 6: 4 Issues (2014)
Volume 5: 4 Issues (2013)
Volume 4: 4 Issues (2012)
Volume 3: 4 Issues (2011)
Volume 2: 4 Issues (2010)
Volume 1: 4 Issues (2009)
View Complete Journal Contents Listing