DocumentCode
2221766
Title
Evolved neural systems with unit breeding. Principles of development
Author
Voronenko, Dmitriy I.
Author_Institution
Kharkov, Ukraine
Volume
1
fYear
1998
fDate
4-8 May 1998
Firstpage
674
Abstract
An approach to evolved neural systems is presented which, although preliminary in nature appears to be able to give a number of advantages over other approaches. The main feature of the approach is evolution under influence of environment. Additionally the more exact simulation of biological evolution is provided. Other features are the continual learning during functioning through self-refinement and self-adjustment and the rules for unsupervised termination of a system existence in the case of its incompetence. The proposition of classification is given for evolved neural systems developed with the presented approach. Two classes of evolved neural system are defined. The first class is the original method of use of genetic algorithm principles to evolution of neural system. The second one is the simulation of evolution of the natural nervous systems
Keywords
evolution (biological); genetic algorithms; neural nets; neurophysiology; self-adjusting systems; biological evolution; evolved neural systems; natural nervous systems; self-adjustment; self-refinement; unit breeding; Artificial neural networks; Biological information theory; Biological system modeling; Biology computing; Computer vision; Evolution (biology); Genetic algorithms; Nervous system; Neural networks; Pattern recognition;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location
Anchorage, AK
ISSN
1098-7576
Print_ISBN
0-7803-4859-1
Type
conf
DOI
10.1109/IJCNN.1998.682361
Filename
682361
Link To Document