DocumentCode
971266
Title
Evolutionary learning of nearest-neighbor MLP
Author
Zhao, Qiangfu ; Higuchi, Tatsuo
Author_Institution
Multimedia Device Lab., Aizu Univ., Japan
Volume
7
Issue
3
fYear
1996
fDate
5/1/1996 12:00:00 AM
Firstpage
762
Lastpage
767
Abstract
The nearest-neighbor multilayer perceptron (NN-MLP) is a single-hidden-layer network suitable for pattern recognition. To design an NN-MLP efficiently, this paper proposes a new evolutionary algorithm consisting of four basic operations: recognition, remembrance, reduction, and review. Experimental results show that this algorithm can produce the smallest or nearly smallest networks from random initial ones
Keywords
learning (artificial intelligence); multilayer perceptrons; pattern recognition; evolutionary learning; nearest-neighbor multilayer perceptron; pattern recognition; reduction; remembrance; review; single-hidden-layer network; Algorithm design and analysis; Counting circuits; Evolutionary computation; Fires; Helium; Iterative algorithms; Multilayer perceptrons; Neurons; Prototypes; Vector quantization;
fLanguage
English
Journal_Title
Neural Networks, IEEE Transactions on
Publisher
ieee
ISSN
1045-9227
Type
jour
DOI
10.1109/72.501733
Filename
501733
Link To Document