Abstract
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 use of 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 learned, one can interpret the fuzzy logic network in order to gain an insight into the nature of rules governing the data.
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Grzymała-Busse, J.W., Goodwin, L.K., Grzymała-Busse, W.J., Zheng, X. (2002). Problems of Rule Induction from Preterm Birth Data. In: Jain, L.C., Kacprzyk, J. (eds) New Learning Paradigms in Soft Computing. Studies in Fuzziness and Soft Computing, vol 84. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1803-1_13
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DOI: https://doi.org/10.1007/978-3-7908-1803-1_13
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