Improving the performance of neural networks in classification using fuzzylinear regression

In this paper, we apply the fuzzy linear regression (FLR) with fuzzy interv als analysis into a neural network classification model. The FLR works as a data handler and separates the data sample into two groups. By training tw o independent neural works with these two groups, we can better describe th e distribution space of the corresponding data sample with two different fu nctions, rather than using only one function. The experimental result shows that our approach improves the accuracy of classification. (C) 2001 Elsevi er Science Ltd. All rights reserved.