Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural Networks - Special issue on neural networks and kernel methods for structured domains
A learning algorithm for continually running fully recurrent neural networks
Neural Computation
The graph neural network model
IEEE Transactions on Neural Networks
Computational capabilities of graph neural networks
IEEE Transactions on Neural Networks
Encoding nondeterministic fuzzy tree automata into recursive neural networks
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
Dealing with structured data has always represented a huge problem for classical neural methods. Although many efforts have been performed, they usually pre-process data and then use classic machine learning algorithm. Another problem that machine learning algorithm have to face is the intrinsic uncertainty of data, where in such situations classic algorithm do not have the means to handle them. In this work a novel neuro-fuzzy model for structured data is presented that exploits both neural and fuzzy methods. The proposed model called Fuzzy Graph Neural Network (F-GNN) is based on GNN, a model able to handle structure data. A proof of F-GNN approximation properties is provided together with a training algorithm.