Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Introduction to artificial neural systems
Introduction to artificial neural systems
Fuzzy logic, neural networks, and soft computing
Communications of the ACM
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
Fuzzy set technology in knowledge discovery
Fuzzy Sets and Systems
An architecture of fuzzy neural networks for linguistic processing
Fuzzy Sets and Systems
Fundamentals of Artificial Neural Networks
Fundamentals of Artificial Neural Networks
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy Sets Engineering
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Clustering interval-valued proximity data using belief functions
Pattern Recognition Letters
A self-organizing feature map-driven approach to fuzzy approximate reasoning
Expert Systems with Applications: An International Journal
An improved fuzzy neural network based on T-S model
Expert Systems with Applications: An International Journal
Some context fuzzy clustering methods for classification problems
Proceedings of the 2010 Symposium on Information and Communication Technology
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A process of information granulation takes care of an enormous flood of numerical details that becomes summarized and hidden (encapsulated in the form of fuzzy sets) at the time of the design of a neural network. Information granules play an important role in the development of neural networks. First, they substantially reduce the amount of training as the designed network needs to deal with a significantly reduced and highly compressed number of data that falls far below the size of the original training set. The same granulation mechanism delivers some highly advantageous regularization properties. Second, information granules support the design of more transparent and easily interpretable neural networks. The necessary effect of information granulation is accomplished in the framework of fuzzy sets, especially via context-sensitive (conditional) fuzzy clustering. Subsequently, the resulting neural network becomes an architecture with nonnumeric connections. A thorough analysis of results of computing carried out in the setting of linguistic neurocomputing is also given.