Evolving logic networks with real-valued inputs for fast incremental learning.
In this paper, we present a neural network structure and a fast incremental learning algorithm using this network. The proposed network structure, named Evolving Logic Networks for Real-valued inputs (ELN-R), is a data structure for storing and using the knowledge. A distinctive feature of ELN-R is that the previously learned knowledge stored in ELN-R can be used as a kind of building block in constructing new knowledge. Using this feature, the proposed learning algorithm can enhance the stability and plasticity at the same time, and as a result, the fast incremental learning can be realized. The performance of the proposed scheme is shown by a theoretical analysis and an experimental study on two benchmark problems.
Park
Myoung Soo
MS
School of Electrical Engineering and Computer Science, Automation and Systems Research Institute, Seoul National University, Seoul 151-744, Korea.
Choi
Jin Young
JY
D000465:Algorithms; D001185:Artificial Intelligence; D016247:Information Storage and Retrieval / Q000379:methods*; D008962:Models, Theoretical; D016571:Neural Networks, Computer