Abstract
The ability of ANNs to learn and generalize from examples, and to generate robust solutions, makes them very suitable in a diversity of applications where algorithmic approaches are either unknown or difficult to implement. A major drawback, however, is that the knowledge learned by the network is represented in an exceedingly opaque form, namely, as a list of numerical coefficients. This black-box character of ANNs hinders the possibility of their more wide-spread acceptance. The problem of extracting the knowledge embedded in the ANN in a comprehensible form has been intensively addressed in the literature.
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© 2009 Springer-Verlag Berlin Heidelberg
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Kolman, E., Margaliot, M. (2009). Conclusions and Future Research. In: Knowledge-Based Neurocomputing: A Fuzzy Logic Approach. Studies in Fuzziness and Soft Computing, vol 234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88077-6_7
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DOI: https://doi.org/10.1007/978-3-540-88077-6_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88076-9
Online ISBN: 978-3-540-88077-6
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