System Maintenance:
There may be intermittent impact on performance while updates are in progress. We apologize for the inconvenience.
By Topic

Supervised and Unsupervised Learning by Using Petri Nets

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Shen, V.R.L. ; Dept. of Comput. Sci. & Inf. Eng., Nat. Taipei Univ., Taipei, Taiwan ; Yue-Shan Chang ; Juang, T.T.-Y.

Artificial neural networks (ANN) are developed for highly parallel and distributed systems. These systems are able to learn from experience and to perform inferences. Although Petri nets (PNs) were modified to be ANN-like multilayered architectures for fuzzy reasoning, some researchers have paid more attention to the PN-based learning so far. In this paper, we have developed supervised and unsupervised learning algorithms for the machine learning PN (MLPN) models in order to make them fully trainable and to remedy the difficulties encountered by ANN. When compared with ANN, the MLPN model shows some significant advantages. Main results are presented in the form of five observations and supported by some experiments.

Published in:

Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on  (Volume:40 ,  Issue: 2 )