Neural network implementation of fuzzy logic
Fuzzy Sets and Systems
A fuzzy neural network for rule acquiring on fuzzy control systems
Fuzzy Sets and Systems - Special issue on fuzzy neural control
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches
IEEE Transactions on Knowledge and Data Engineering
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
A neuro-fuzzy scheme for simultaneous feature selection and fuzzy rule-based classification
IEEE Transactions on Neural Networks
FCMAC-Yager: A Novel Yager-Inference-Scheme-Based Fuzzy CMAC
IEEE Transactions on Neural Networks
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The strength of neuro-fuzzy systems involves two contradictory requirements in neuro-fuzzy modeling: interpretability versus accuracy. The Yager-Inference-Scheme-Based Fuzzy CMAC (FCMAC-Yager) architecture shows advantages such as it exhibits learning and memory capabilities of the human cerebellum through the CMAC (cerebellar model articulation controller) structure and the human way of reasoning through the Yager inference scheme. However, it suffered from an exponential increase in the number of identified fuzzy rules and computational cost arising from high-dimensional data. This diminishes the interpretability of the FCMAC-Yager network in linguistic fuzzy modeling. This paper proposes a novel rough set-based rule reduction (RSFCMAC) approach for the established FCMAC-Yager architecture. RSFCMAC algorithm used in the FCMAC-Yager network can help to provide better generalization, to reduce the number of fuzzy rules and computational cost. The proposed algorithm not only performs reduction of redundant fuzzy rules, but also carries out reduction of redundant input attributes. Experiments using real-world application involving stock movement and highway traffic flow prediction were conducted to evaluate the performance of the proposed RSFCMAC against the FCMAC-Yager network and other published results of cross-architectures using globalized learning as well as similar architectures employing localized learning. The results are encouraging.