A neuro-fuzzy model of adaptive learning and feature detection is presented. The model, called the fuzzy filtered neural network, was first introduced in a previous publication, which showed its validity in the domain of plasma analysis. Here we extend the model to another problem, the recognition of hand-written numerals, to demonstrate its generality. We propose three versions of the architecture, which use one-dimensional fuzzy filters, two-dimensional fuzzy filters, and genetic-algorithm-based fuzzy filters, respectively, as feature detectors. All three versions smoothly handle such issues of a real-world pattern recognition problem as drifting and noise. Simulation results show that the proposed model is an efficient architecture for achieving high recognition accuracy.
|Number of pages||5|
|State||Published - 1 Dec 1994|
|Event||Proceedings of the 3rd IEEE Conference on Fuzzy Systems. Part 3 (of 3) - Orlando, FL, USA|
Duration: 26 Jun 1994 → 29 Jun 1994
|Conference||Proceedings of the 3rd IEEE Conference on Fuzzy Systems. Part 3 (of 3)|
|City||Orlando, FL, USA|
|Period||26/06/94 → 29/06/94|