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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Casalino, P. Masulli, F. Sperduti, A. Vannucci, F. |
| Copyright Year | 1994 |
| Description | Author affiliation: Dept. of Comput. & Inf. Sci., Genoa Univ., Italy (Casalino, P.) |
| Abstract | We investigate how many rules a particular adaptive fuzzy system, very similar to a feedforward neural network, needs to perform digit classification. Both training of systems with different numbers of fuzzy rules and the application of a pruning technique to a non minimal system support the statement that one rule for each class is needed. In particular, a semantic phase transition is observed when a new rule is added to a system with 9 rules. This behaviour, which is not common for a feedforward neural network, could be ascribed to the derivation of the studied system from a fuzzy logic framework.< |
| Starting Page | 808 |
| Ending Page | 812 |
| File Size | 431049 |
| Page Count | 5 |
| File Format | |
| ISBN | 078031896X |
| DOI | 10.1109/FUZZY.1994.343839 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1994-06-26 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Adaptive systems Fuzzy systems Neural networks Feedforward neural networks Fuzzy neural networks Feedforward systems Fuzzy logic Process control Physics Computer science |
| Content Type | Text |
| Resource Type | Article |
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