Abstract
A new learning algorithm for Evolving Cascade Neural Networks (ECNNs) is described. An ECNN starts to learn with one input node and then adding new inputs as well as new hidden neurons evolves it. The trained ECNN has a nearly minimal number of input and hidden neurons as well as connections. The algorithm was successfully applied to classify artifacts and normal segments in clinical electroencephalograms (EEGs). The EEG segments were visually labeled by EEG-viewer. The trained ECNN has correctly classified 96.69% of the testing segments. It is slightly better than a standard fully connected neural network.
| Original language | English |
|---|---|
| Pages (from-to) | 21-31 |
| Number of pages | 11 |
| Journal | Neural Processing Letters |
| Volume | 17 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Feb 2003 |
Keywords
- Cascade architecture
- Electroencephalogram
- Evolving
- Feature selection
- Neural network
ASJC Scopus subject areas
- Software
- General Neuroscience
- Computer Networks and Communications
- Artificial Intelligence
Fingerprint
Dive into the research topics of 'A learning algorithm for evolving cascade neural networks'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver