Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
Classifier systems and genetic algorithms
Machine learning: paradigms and methods
Communications of the ACM
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
A Closest Fit Approach to Missing Attribute VAlues in Preterm Birth Data
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
On the Unknown Attribute Values in Learning from Examples
ISMIS '91 Proceedings of the 6th International Symposium on Methodologies for Intelligent Systems
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This study is concerned with the design and analysis of digital systems using fuzzy neurocomputing. Some basic definitions of fuzzy-logic neurons, fundamental neural architectures, learning strategies and interpretation of their results are presented. We promote a concept of embedding principle: an original Boolean problem is represented in the language of fuzzy sets, afterwards solved through learning, and, finally, the result of learning re-interpreted in terms of two-valued logic. We show the fuzzy neurons in a standard design of combinational systems including a minimization of incompletely specified Boolean functions (viz. Those involving don't care conditions) and Boolean functions with many outputs. It is also claimed that this approach supports reverse engineering in the sense that once the architecture of the Boolean circuit has been leaned, one can interpret the fuzzy logic network in order to gain an insight into the nature of rules governing the data.