A qualitative physics based on confluences
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Fuzzy mathematical techniques with applications
Fuzzy mathematical techniques with applications
A characterization of the extension principle
Fuzzy Sets and Systems - Special issue: Dedicated to the memory of Richard E. Bellman
On several definitions of the differential of a fuzzy mapping
Fuzzy Sets and Systems
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Fuzzy topological vector spaces-topological generation and normability
Fuzzy Sets and Systems
A law of large numbers for fuzzy numbers
Fuzzy Sets and Systems
An introduction to fuzzy control
An introduction to fuzzy control
Ordering, distance and closeness of fuzzy sets
Fuzzy Sets and Systems - Special issue on fuzzy data analysis
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and information granularity
Fuzzy sets, fuzzy logic, and fuzzy systems
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
Methods for decision making with cardinal numbers and additive aggregation
Fuzzy Sets and Systems
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Operations on fuzzy numbers via fuzzy reasoning
Fuzzy Sets and Systems - Special issue: fuzzy arithmetic
Fuzzy numbers are the only fuzzy sets that keep invertible operations invertible
Fuzzy Sets and Systems - Special issue: fuzzy arithmetic
A parametric representation of fuzzy numbers and their arithmetic operators
Fuzzy Sets and Systems - Special issue: fuzzy arithmetic
Analytical expressions for the addition of fuzzy intervals
Fuzzy Sets and Systems - Special issue: fuzzy arithmetic
Similarity preserving t-norm-based additions of fuzzy numbers
Fuzzy Sets and Systems - Special issue: fuzzy arithmetic
Triangular-norm-based addition of fuzzy intervals
Fuzzy Sets and Systems - Special issue: fuzzy arithmetic
A T-sum bound of LR-fuzzy numbers
Fuzzy Sets and Systems - Special issue: fuzzy arithmetic
A note to the addition of fuzzy numbers based on a continuous Archimedean T-norm
Fuzzy Sets and Systems - Special issue: fuzzy arithmetic
Fuzzy aggregation of numerical preferences
Fuzzy sets in decision analysis, operations research and statistics
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Qualitative Approach to Gradient Based Learning Algorithms
IWANN '96 Proceedings of the International Workshop on Artificial Neural Networks: From Natural to Artificial Neural Computation
A connectionist approach to generating oblique decision trees
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Linguistic models and linguistic modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
A parametric model for fusing heterogeneous fuzzy data
IEEE Transactions on Fuzzy Systems
Reconstruction problem and information granularity
IEEE Transactions on Fuzzy Systems
Two nonparametric models for fusing heterogeneous fuzzy data
IEEE Transactions on Fuzzy Systems
Implementation of evolutionary fuzzy systems
IEEE Transactions on Fuzzy Systems
SLAVE: a genetic learning system based on an iterative approach
IEEE Transactions on Fuzzy Systems
Fuzzy rough granular neural networks, fuzzy granules, and classification
Theoretical Computer Science
A similarity measure for fuzzy rulebases based on linguistic gradients
Information Sciences: an International Journal
A granular neural network: Performance analysis and application to re-granulation
International Journal of Approximate Reasoning
Hi-index | 0.00 |
The basic premise of granular computing is that, by reducing precision in our model of a system, we can suppress minor details and focus on the most significant relationships in the system. In this chapter, we will test this premise by defining a granular neural network and testing it on the Iris data set. Our hypothesis is that the granular neural network will be able to learn the Iris data set, but not as accurately as a standard neural network. Our network is a novel neurofuzzy systems architecture called the linguistic neural network. The defining characteristic of this network is that all connection weights are linguistic variables, whose values are updated by adding linguistic hedges. We define two new hedges, whose semantics require a generalization of the standard definition of linguistic variables. These generalized linguistic variables lead naturally to a linguistic arithmetic, which we prove forms a vector space. The node functions of the linguistic neural network are defined in terms of this linguistic arithmetic. The learning method used for the network is a modified Backpropagation algorithm, with the original arithmetic operations replaced by their linguistic equivalents. In a simulation experiment, this granulated version of the multilayer perceptron achieved 90% accuracy on the Iris data set, using a coarse granulation. This result supports our hypothesis.