Application of fuzzy-rough sets in modular neural networks

In a modular neural network, the conflicting information supplied by the information sources, i.e., the outputs of the subnetworks, can be combined by applying the concept of fuzzy integral. To compute the fuzzy integral it is essential to know the importance of each subset of the information sources in a quantified form. In practice, it is very difficult to determine the level of the information sources. However, in the fuzzy integral approach the importance of a particular information source is considered to be independent of the other information sources. Therefore, determination of the importance of each information source should be based on the incomplete knowledge supplied by the source itself. This paper proposes a fuzzy-rough set theoretic approach to find the importance of each subset of the information sources from this incomplete knowledge.