Fuzzy logic, neural networks, and soft computing
Communications of the ACM
Information Sciences: an International Journal
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Rough-Fuzzy Hybridization: A New Trend in Decision Making
Rough-Fuzzy Hybridization: A New Trend in Decision Making
Granular computing in neural networks
Granular computing
Granular neural networks for land use classification
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Generalized fuzzy rough sets determined by a triangular norm
Information Sciences: an International Journal
The many facets of natural computing
Communications of the ACM
Attribute selection with fuzzy decision reducts
Information Sciences: an International Journal
Generalized rough sets, entropy, and image ambiguity measures
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
New approaches to fuzzy-rough feature selection
IEEE Transactions on Fuzzy Systems
Granular Neural Networks With Evolutionary Interval Learning
IEEE Transactions on Fuzzy Systems
Rough fuzzy MLP: knowledge encoding and classification
IEEE Transactions on Neural Networks
Granular neural networks for numerical-linguistic data fusion and knowledge discovery
IEEE Transactions on Neural Networks
Multilayer perceptron, fuzzy sets, and classification
IEEE Transactions on Neural Networks
Fuzzy rough granular self organizing map
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Fuzzy rough granular self-organizing map and fuzzy rough entropy
Theoretical Computer Science
Title Natural computing: A problem solving paradigm with granular information processing
Applied Soft Computing
Four matroidal structures of covering and their relationships with rough sets
International Journal of Approximate Reasoning
Hi-index | 5.23 |
We introduce a fuzzy rough granular neural network (FRGNN) model based on the multilayer perceptron using a back-propagation algorithm for the fuzzy classification of patterns. We provide the development strategy of the network mainly based upon the input vector, initial connection weights determined by fuzzy rough set theoretic concepts, and the target vector. While the input vector is described in terms of fuzzy granules, the target vector is defined in terms of fuzzy class membership values and zeros. Crude domain knowledge about the initial data is represented in the form of a decision table, which is divided into subtables corresponding to different classes. The data in each decision table is converted into granular form. The syntax of these decision tables automatically determines the appropriate number of hidden nodes, while the dependency factors from all the decision tables are used as initial weights. The dependency factor of each attribute and the average degree of the dependency factor of all the attributes with respect to decision classes are considered as initial connection weights between the nodes of the input layer and the hidden layer, and the hidden layer and the output layer, respectively. The effectiveness of the proposed FRGNN is demonstrated on several real-life data sets.