Abstract
This article presents a survey of models of rough neurocomputing that have their roots in rough set theory. Historically, rough neurocomputing has three main threads: training set production, calculus of granules, and interval analysis. This form of neurocomputing gains its inspiration from the work of Pawlak on rough set philosophy as a basis for machine learning and from work on data mining and pattern recognition by Swiniarski and others in the early 1990s. This work has led to a variety of new rough neurocomputing computational models that are briefly presented in this article. The contribution of this article is a survey of representative approaches to rough neurocomputing.
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Peters, J.F., Szczuka, M.S. (2002). Rough Neurocomputing: A Survey of Basic Models of Neurocomputation. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds) Rough Sets and Current Trends in Computing. RSCTC 2002. Lecture Notes in Computer Science(), vol 2475. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45813-1_40
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DOI: https://doi.org/10.1007/3-540-45813-1_40
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