Extracting symbolic knowledge from recurrent neural networks---A fuzzy logic approach
Fuzzy Sets and Systems
Analysis of artificial neural network learning near temporary minima: A fuzzy logic approach
Fuzzy Sets and Systems
Improving fuzzy knowledge integration with particle swarmoptimization
Expert Systems with Applications: An International Journal
A probabilistic fuzzy approach to modeling nonlinear systems
Neurocomputing
A parallel evolving algorithm for flexible neural tree
Parallel Computing
Swarm optimization and Flexible Neural Tree for microarray data classification
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
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A major drawback of artificial neural networks (ANNs) is their black-box character. Even when the trained network performs adequately, it is very difficult to understand its operation. In this letter, we use the mathematical equivalence between ANNs and a specific fuzzy rule base to extract the knowledge embedded in the network. We demonstrate this using a benchmark problem: the recognition of digits produced by a light emitting diode (LED) device. The method provides a symbolic and comprehensible description of the knowledge learned by the network during its training