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Supervised and Unsupervised Learning by Using Petri Nets

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3 Author(s)
Victor R. L. Shen ; Department of Computer Science and Information Engineering, National Taipei University, Taipei, Taiwan ; Yue-Shan Chang ; Tony Tong-Ying Juang

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:

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