Toward Intelligent Systems: Calculi of Information Granules
Proceedings of the Joint JSAI 2001 Workshop on New Frontiers in Artificial Intelligence
Rough Neurocomputing: A Survey of Basic Models of Neurocomputation
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Approximate Reasoning by Agents
CEEMAS '01 Revised Papers from the Second International Workshop of Central and Eastern Europe on Multi-Agent Systems: From Theory to Practice in Multi-Agent Systems
A Rough CP Neural Network Model Based on Rough Set
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 01
Missing Attribute Value Prediction Based on Artificial Neural Network and Rough Set Theory
BMEI '08 Proceedings of the 2008 International Conference on BioMedical Engineering and Informatics - Volume 01
A Study of Classification Algorithm for Data Mining Based on Hybrid Intelligent Systems
SNPD '08 Proceedings of the 2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing
Rough Neuron Based Neural Classifier
ICETET '08 Proceedings of the 2008 First International Conference on Emerging Trends in Engineering and Technology
Review: Dimensionality reduction based on rough set theory: A review
Applied Soft Computing
Decision Analysis of Combat Effectiveness Based on Rough Set Neural Network
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 07
ICIC '09 Proceedings of the 2009 Second International Conference on Information and Computing Science - Volume 03
Application of Rough Set and Fuzzy Neural Network in Information Handling
ICNDS '09 Proceedings of the 2009 International Conference on Networking and Digital Society - Volume 02
ICSPS '09 Proceedings of the 2009 International Conference on Signal Processing Systems
Optimum design of structures by an improved genetic algorithm using neural networks
Advances in Engineering Software - Selected papers from civil-comp 2003 and AlCivil-comp 2003
A hybrid forecast marketing timing model based on probabilistic neural network, rough set and C4.5
Expert Systems with Applications: An International Journal
The Research on CMAC Network Model Based on Rough Sets for Flatness Control
ICICIC '09 Proceedings of the 2009 Fourth International Conference on Innovative Computing, Information and Control
Approximation and prediction of wages based on granular neural network
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
The application of rough neural network in RMF model
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 1
Particle swarm optimization neural network and its application in soft-sensing modeling
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Granular Neural Networks With Evolutionary Interval Learning
IEEE Transactions on Fuzzy Systems
OR/AND neuron in modeling fuzzy set connectives
IEEE Transactions on Fuzzy Systems
Granular neural networks for numerical-linguistic data fusion and knowledge discovery
IEEE Transactions on Neural Networks
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Fuzzy neural networks (FNNs) and rough neural networks (RNNs) both have been hot research topics in the artificial intelligence in recent years. The former imitates the human brain in dealing with problems, the other takes advantage of rough set theory to process questions uncertainly. The aim of FNNs and RNNs is to process the massive volume of uncertain information, which is widespread applied in our life. This article summarizes the recent research development of FNNs and RNNs (together called granular neural networks). First the fuzzy neuron and rough neuron is introduced; next FNNs are analysed in two categories: normal FNNs and fuzzy logic neural networks; then the RNNs are analysed in the following four aspects: neural networks based on using rough sets in preprocessing information, neural networks based on rough logic, neural networks based on rough neuron and neural networks based on rough-granular; then we give a flow chart of the RNNs processing questions and an application of classical neural networks based on rough sets; next this is compared with FNNs and RNNs and the way to integrate is described; finally some advice is given on development of FNNs and RNNs in future.