Method for detecting and classifying anomalies using artificial neural networks
US6622135B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 16, 1999 |
| Grant date | Sep 16, 2003 |
| Priority date | — |
| Expiry date | Dec 16, 2019 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30148
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
To avoid the problem of category assignment in artificial neural networks (ANNs) based upon a mapping of the input space (like ROI and KNN algorithms), the present method uses “probabilities”. Now patterns memorized as prototypes do not represent categories any longer but the “probabilities” to belong to categories. Thus, after having memorized the most representative patterns in a first step of the learning phase, the second step consists of an evaluation of these probabilities. To that end, several counters are associated with each prototype and are used to evaluate the response frequency and accuracy for each neuron of the ANN. These counters are dynamically incremented during this second step using distances evaluation (between the input vectors and the prototypes) and error criteria (for example the differences between the desired responses and the response given by the ANN). At the end of the learning phase, a function of the contents of these counters allows an evaluation of these probabilities for each neuron to belong to predetermined categories. During the recognition phase, the probabilities associated with the neurons selected by the algorithm pe…
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