Symbolic Rule Representation in Neural Network Models
نویسندگان
چکیده
Symbolic knowledge extraction from mapping/extrapolating neural networks is presented in the paper. An algorithm to obtain crisp rules in the form of logical implications which roughly describe the neural network mapping is introduced. The number of extracted rules can be selected using an uncertainty margin parameter as well as by changing the precision of the soft quantization of the inputs. A fuzzy decision system for a medication dosage problem has been developed and tested to demonstrate this approach.
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