Fuzzy Petri nets for rule-based decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
Petri nets with uncertain markings
APN 90 Proceedings on Advances in Petri nets 1990
A fuzzy Petri net for knowledge representation and reasoning
Information Processing Letters
Artificial intelligence and soft computing: behavioral and cognitive modeling of the human brain
Artificial intelligence and soft computing: behavioral and cognitive modeling of the human brain
Fuzzy Sets Engineering
Knowledge Representation Using Fuzzy Petri Nets
IEEE Transactions on Knowledge and Data Engineering
Uncertainty Management in Expert Systems Using Fuzzy Petri Nets
IEEE Transactions on Knowledge and Data Engineering
Logic - Oriented Fuzzy Neural Networks
International Journal of Hybrid Intelligent Systems
Cognitive reasoning using fuzzy neural nets
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy backward reasoning using fuzzy Petri nets
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A reasoning algorithm for high-level fuzzy Petri nets
IEEE Transactions on Fuzzy Systems
A hybrid machine learning approach to network anomaly detection
Information Sciences: an International Journal
The modified fuzzy art and a two-stage clustering approach to cell design
Information Sciences: an International Journal
Hi-index | 0.00 |
Feed-forward neural networks used for pattern classification generally have one input layer, one output layer and several hidden layers. The hidden layers in these networks add extra non-linearity for realization of precise functional mapping between the input and the output layers, but semantic relations of the hidden layers with their predecessor and successor layers cannot be justified. This paper presents a novel scheme for supervised learning on a fuzzy Petri net that provides semantic justification of the hidden layers, and is capable of approximate reasoning and learning from noisy training instances. An algorithm for training a feed-forward fuzzy Petri net and an analysis of its convergence have been presented in the paper. The paper also examines the scope of the learning algorithm in object recognition from 2D geometric views.