Abstract
Neural networks using a piecewise linear ramp-step activation function may be interpreted as expressions in the propositional logic of Kleene-Lukasiewicz. These expressions even though information preserving may have a high degree of complexity impairing their understandability. The paper discloses a strategy which combines classical logic with the logic of Lukasiewicz to decompose a complex rule into a set of simpler rules that cover the former.
Work leading to this paper has been partially supported by the Ministry of Education and Research, Germany, under grant BMBF-CH-99/023 and by the Technical University Federico Santa María, Chile, under grant UTFSM/DGIP-Intelligent Data Mining in Complex Systems/2000-2001.
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© 2001 Springer-Verlag Berlin Heidelberg
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Moraga, C., Salinas, L. (2001). Interpreting Neural Networks in the Frame of the Logic of Lukasiewicz. In: Mira, J., Prieto, A. (eds) Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence. IWANN 2001. Lecture Notes in Computer Science, vol 2084. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45720-8_17
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DOI: https://doi.org/10.1007/3-540-45720-8_17
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