Extraction of fuzzy rules from support vector machines
Fuzzy Sets and Systems
Extracting symbolic knowledge from recurrent neural networks---A fuzzy logic approach
Fuzzy Sets and Systems
Improving Training in the Vicinity of Temporary Minima
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Connectionist Models for Formal Knowledge Adaptation
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
On the equivalence of single input type fuzzy inference methods
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on recent advances in biometrics
Analysis of artificial neural network learning near temporary minima: A fuzzy logic approach
Fuzzy Sets and Systems
Equivalences between neural-autoregressive time series models and fuzzy systems
IEEE Transactions on Neural Networks
Rule extraction for support vector machine using input space expansion
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Prediction of the Amount of Wood Using Neural Networks
Journal of Mathematical Modelling and Algorithms
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
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In this paper, we introduce a novel Mamdani-type fuzzy model, referred to as the all-permutations fuzzy rule base (APFRB), and show that it is mathematically equivalent to a standard feedforward neural network. We describe several applications of this equivalence between a neural network and our fuzzy rule base (FRB), including knowledge extraction from and knowledge insertion into neural networks.