Real time fuzzy personalized web stock information agent
Second international workshop on Intelligent systems design and application
Web-based fuzzy neural networks for stock prediction
Second international workshop on Intelligent systems design and application
Statistical fuzzy interval neural networks for currency exchange rate time series prediction
Applied Soft Computing
Genetic Granular Neural Networks
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Granular support vector machines with association rules mining for protein homology prediction
Artificial Intelligence in Medicine
Genetic granular cognitive fuzzy neural networks and human brains for pattern recognition
WImBI'06 Proceedings of the 1st WICI international conference on Web intelligence meets brain informatics
An application of fuzzy information granulation in the emerging area of online sports
Expert Systems with Applications: An International Journal
Robust granular neural networks, fuzzy granules and classification
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Fuzzy rough granular neural networks, fuzzy granules, and classification
Theoretical Computer Science
Knowledge discovery by an intelligent approach using complex fuzzy sets
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part I
Evolving granular neural networks from fuzzy data streams
Neural Networks
A granular neural network: Performance analysis and application to re-granulation
International Journal of Approximate Reasoning
Artificial Intelligence Review
A Cooperative Intrusion Detection Model Based on Granular Computing and Agent Technologies
International Journal of Agent Technologies and Systems
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We present a neural-networks-based knowledge discovery and data mining (KDDM) methodology based on granular computing, neural computing, fuzzy computing, linguistic computing, and pattern recognition. The major issues include 1) how to make neural networks process both numerical and linguistic data in a database, 2) how to convert fuzzy linguistic data into related numerical features, 3) how to use neural networks to do numerical-linguistic data fusion, 4) how to use neural networks to discover granular knowledge from numerical-linguistic databases, and 5) how to use discovered granular knowledge to predict missing data. In order to answer the above concerns, a granular neural network (GNN) is designed to deal with numerical-linguistic data fusion and granular knowledge discovery in numerical-linguistic databases. From a data granulation point of view the GNN can process granular data in a database. From a data fusion point of view, the GNN makes decisions based on different kinds of granular data. From a KDDM point of view the GNN is able to learn internal granular relations between numerical-linguistic inputs and outputs, and predict new relations in a database. The GNN is also capable of greatly compressing low-level granular data to high-level granular knowledge with some compression error and a data compression rate. To do KDDM in huge databases, parallel GNN and distributed GNN will be investigated in the future