Extract intelligible and concise fuzzy rules from neural networks
Fuzzy Sets and Systems - Fuzzy systems
Automatic Recurrent and Feed-Forward ANN Rule and Expression Extraction with Genetic Programming
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Functional equivalence between S-neural networks and fuzzy models
Technologies for constructing intelligent systems
Extracting symbolic rules from trained neural network ensembles
AI Communications - Special issue on Artificial intelligence advances in China
Extracting symbolic rules from trained neural network ensembles
AI Communications - Artificial Intelligence Advances in China
Nicholson's blowflies revisited: A fuzzy modeling approach
Fuzzy Sets and Systems
Extraction of fuzzy rules from support vector machines
Fuzzy Sets and Systems
Interpretable Piecewise Linear Classifier
Neural Information Processing
Extracting symbolic knowledge from recurrent neural networks---A fuzzy logic approach
Fuzzy Sets and Systems
How does the Dendrocoleum lacteum orient to light? A fuzzy modeling approach
Fuzzy Sets and Systems
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
Numerosity and the consolidation of episodic memory
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on recent advances in biometrics
On the monotonicity of fuzzy-inference methods related to T-S inference method
IEEE Transactions on Fuzzy Systems - Special section on computing with words
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
Some remarks on the characterization of idempotent uninorms
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Fuzzy pairwise multiclass support vector machines
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
The pattern classification based on fuzzy min-max neural network with new algorithm
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
Extracting reducible knowledge from ANN with JBOS and FCANN approaches
Expert Systems with Applications: An International Journal
A Probabilistic Neural Network-Based Module for Recognition of Objects from their 3-D Images
International Journal of System Dynamics Applications
Advances in Fuzzy Systems - Special issue on Real-Life Applications of Fuzzy Logic
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Artificial neural networks are efficient computing models which have shown their strengths in solving hard problems in artificial intelligence. They have also been shown to be universal approximators. Notwithstanding, one of the major criticisms is their being black boxes, since no satisfactory explanation of their behavior has been offered. In this paper, we provide such an interpretation of neural networks so that they will no longer be seen as black boxes. This is stated after establishing the equality between a certain class of neural nets and fuzzy rule-based systems. This interpretation is built with fuzzy rules using a new fuzzy logic operator which is defined after introducing the concept of f-duality. In addition, this interpretation offers an automated knowledge acquisition procedure