The functional equations of Frank and Alsina for uninorms and nullnorms
Fuzzy Sets and Systems
ESOM: An Algorithm to Evolve Self-Organizing Maps from On-Line Data Streams
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Evolving Connectionist Systems: The Knowledge Engineering Approach
Evolving Connectionist Systems: The Knowledge Engineering Approach
Neurons and Neural Fuzzy Networks Based on Nullnorms
SBRN '08 Proceedings of the 2008 10th Brazilian Symposium on Neural Networks
Handbook of Granular Computing
Handbook of Granular Computing
Fuzzy Systems Engineering: Toward Human-Centric Computing
Fuzzy Systems Engineering: Toward Human-Centric Computing
Evolving fuzzy neural networks for supervised/unsupervised onlineknowledge-based learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
Logic-Based Fuzzy Neurocomputing With Unineurons
IEEE Transactions on Fuzzy Systems
Granular Neural Networks With Evolutionary Interval Learning
IEEE Transactions on Fuzzy Systems
Evolving Fuzzy-Rule-Based Classifiers From Data Streams
IEEE Transactions on Fuzzy Systems
General fuzzy min-max neural network for clustering and classification
IEEE Transactions on Neural Networks
Granular approach for evolving system modeling
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
A granular neural network: Performance analysis and application to re-granulation
International Journal of Approximate Reasoning
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The objective of this study is to introduce the concept of evolving granular neural networks (eGNN) and to develop a framework of information granulation and its role in the online design of neural networks. The suggested eGNN are neural models supported by granule-based learning algorithms whose aim is to tackle classification problems in continuously changing environments. eGNN are constructed from streams of data using fast incremental learning algorithms. eGNN models require a relatively small amount of memory to perform classification tasks. Basically, they try to find information occurring in the incoming data using the concept of granules and T-S neurons as basic processing elements. The main characteristics of eGNN models are continuous learning, self-organization, and adaptation to unknown environments. Association rules and parameters can be easily extracted from its structure at any step during the evolving process. The rule base gives a granular description of the behavior of the system in the input space together with the associated classes. To illustrate the effectiveness of the approach, the paper considers the Iris and Wine benchmark problems.