@INPROCEEDINGS { Rsds2700,
author = {Szczuka, Marcin S. and Peters, James F.}, 
title = {Rough Neurocomputing: A Survey of Basic Models of Neurocomputation}, 
booktitle = {Rough Sets and Current Trends in Computing}, 
conference = {Rough Sets and Current Trends in Computing}, 
series =  = {Lecture Notes in Arificial Intelligence}, 
volume = 2475, 
pages = {308--315}, 
publisher = {Springer-Verlag}, 
address = {Heidelberg - Berlin, Germany}, 
month = {October}, 
year = 2002, 
editor = {Alpigini, James J. and Peters, James F. and Skowron, Andrzej and Zhong, Ning Liu Jiming}, 
issn = {0302-9743}, 
isbn = {3-540-44274-X}, 
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.}, 
keywords = {information granule, mereology, neural network, rough sets, }, 
}