Abstract
We propose a learning algorithm for selforganizing neural networks to form a topology preserving map from an input manifold whose topology may dynamically change. Experimental results show that the network using the proposed algorithm can rapidly adjust itself to represent the topology of nonstationary input distributions.
Original language | English |
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Pages (from-to) | 1131-1135 |
Number of pages | 5 |
Journal | IEICE Transactions on Information and Systems |
Volume | E82-D |
Issue number | 7 |
Publication status | Published - 1999 Jan 1 |
Keywords
- Competitive Hebbian learning rule
- Law of the jungle mechanism
- Neural networks
- Nonstationary probability distribution
- Self-organizing map
ASJC Scopus subject areas
- Software
- Hardware and Architecture
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering
- Artificial Intelligence