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Rough Neural Networks (1996)
| Venue: | In: Proc. of the 6th Int. Conf. on Information Processing and Management of Uncertainty in Knowledge-based Systems (IPMU96 |
| Citations: | 17 - 1 self |
BibTeX
@INPROCEEDINGS{Lingras96roughneural,
author = {Pawan Lingras},
title = {Rough Neural Networks},
booktitle = {In: Proc. of the 6th Int. Conf. on Information Processing and Management of Uncertainty in Knowledge-based Systems (IPMU96},
year = {1996},
pages = {1445--1450}
}
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Abstract
This paper describes rough neural networks which consists of a combination of rough neurons and conventional neurons. Rough neurons use pairs of upper and lower bounds as values for input and output. In some practical situations, it is preferable to develop prediction models that use ranges as values for input and/or output variables. A need to provide tolerance ranges is an example of such a situation. Inability to record precise values of the variables is another situation where ranges of values must be used. In the example used in this study, a number of input values are associated with a single value of the output variable. Hence, it seems appropriate to represent the input values as ranges. The predictions obtained using rough neural networks are significantly better than the conventional neural network model. 2 1. Introduction The concept of upper and lower bound has been used in a variety of applications in artificial intelligence (Shafer 1976; Pawlak 1982). In particular, th...
