Abstract
This paper presents a neural network with variable parameters. These variable parameters adapt to the changes of the input environment, and tackle different input data sets in a large domain. Each input data set is effectively handled by its corresponding set of network parameters. Thus, the proposed neural network exhibits a better learning and generalization ability than a traditional one. An improved genetic algorithm [1] is proposed to train the network parameters. An application example on hand-written pattern recognition will be presented to verify and illustrate the improvement.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the International Joint Conference on Neural Networks |
| Pages | 1343-1348 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 1 Dec 2005 |
| Event | International Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada Duration: 31 Jul 2005 → 4 Aug 2005 |
Conference
| Conference | International Joint Conference on Neural Networks, IJCNN 2005 |
|---|---|
| Country/Territory | Canada |
| City | Montreal, QC |
| Period | 31/07/05 → 4/08/05 |
ASJC Scopus subject areas
- Software
Fingerprint
Dive into the research topics of 'A variable-parameter neural network trained by improved genetic algorithm and its application'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver