A neurocomputational perspective
A neurocomputational perspective
Comparison of neofuzzy and rough neural networks
Information Sciences: an International Journal
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Design of Rough Neurons: Rough Set Foundation and Petri Net Model
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Rough Neural Networks for Complex Concepts
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Feedforward neural networks for compound signals
Theoretical Computer Science
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This paper proposes a novel method of combining Rough concepts with Neural Computation. The proposed New Rough Neuron consists of, one Lower Bound Neuron and another Boundary Neuron. The combination is designed in a way such that the Boundary Neuron deals only with the random and unpredictable part of the applied signal. This division results in an improved rate of error convergence in the back propagation of the neural network along with an improved parameter approximation during the network learning process. Some testing results have been presented, and performance compared with some of the prevalent designs.