Fuzzy mathematical techniques with applications
Fuzzy mathematical techniques with applications
Fuzzy neural networks: a survey
Fuzzy Sets and Systems
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
An architecture of fuzzy neural networks for linguistic processing
Fuzzy Sets and Systems
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Fuzzy Modelling: Paradigms and Practices
Fuzzy Modelling: Paradigms and Practices
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Mathematical Methods for Neural Network Analysis and Design
Mathematical Methods for Neural Network Analysis and Design
Fuzzy and Neural Approaches in Engineering
Fuzzy and Neural Approaches in Engineering
Fuzzy Sets Engineering
Foundations of Fuzzy Systems
Intelligent Control: Aspects of Fuzzy Logic and Neural Nets
Intelligent Control: Aspects of Fuzzy Logic and Neural Nets
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
Fuzzy lattice reasoning (FLR) classifier and its application for ambient ozone estimation
International Journal of Approximate Reasoning
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The study is concerned with the fundamentals of granular computing and its application to neural networks. Granular computing, as the name itself stipulates, deals with representing information in the forms of some aggregates (embracing a number of individual entities) and their ensuing processing. We elaborate on the rationale behind granular computing. Next, a number of formal frameworks of information granulation are discussed including several alternatives such as fuzzy sets, internal analysis, rough sets, and probability. The notion of granularity itself is defined and quantified. A design agenda of granular computing is formulated and the key design problems are raised. A number of granular architectures are also discussed with an objective of dealinating the fundamental algorithmic and conceptual challenges. It is shown that the use of information granules of different size (granularity) lends itself to general pyramid architectures of information processing. The role of encoding and decoding mechanisms visible in this setting is also discussed in detail along with some particular solutions. Neural networks are primarily involved at the level of numeric optimization. Granularity of information introduces another dimension to the neurocomputing. We discuss the role of granular constructs in the design of neural networks and knowledge representation therein. The intent of this paper is to elaborate on the fundamentals and put the entire area in a certain perspective while not moving into specific algorithmic details.