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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Vachkov, G. |
| Copyright Year | 2007 |
| Description | Author affiliation: Kagawa Univ., Kagawa (Vachkov, G.) |
| Abstract | In this paper a new algorithm for creating evolving neural models is proposed. Instead of repeating the "growing" and "pruning" steps during the learning, as in the most other known evolving algorithms, here we create a growing neural model from a fixed size data buffer and repeatedly check the model quality, in the sense of "average minimal distance" between the neurons and the data in this buffer. If the current neural model does not satisfy the required quality for the new on-line updated data buffer, then a new growing neural model is created from the updated data buffer. The new proposed algorithm is very efficient in computation cost. Its better performance is demonstrated by comparison with two other algorithms with preliminary fixed number of neurons, namely the standard non-evolving learning and the real-time evolving algorithm. The results show the applicability of the new algorithm for modeling and detection of changes and abnormalities in real-time evolving processes. |
| Starting Page | 382 |
| Ending Page | 382 |
| File Size | 485993 |
| Page Count | 1 |
| File Format | |
| ISBN | 0769528821 |
| DOI | 10.1109/ICICIC.2007.420 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-09-05 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Fault diagnosis Change detection algorithms Shape Diesel engines Neurons Cities and towns Reliability engineering Systems engineering and theory Computer buffers Information systems |
| Content Type | Text |
| Resource Type | Article |
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