DocumentCode :
394388
Title :
Bounding NBLM neighbourhood´s adequate sizes
Author :
Mayoral, R. ; Lera, G.
Author_Institution :
Dept. Automatica y Computacion, Univ. Publica de Navarra, Pamplona, Spain
Volume :
4
fYear :
2002
fDate :
18-22 Nov. 2002
Firstpage :
1676
Abstract :
We try to address the problem of a priori selection of the adequate size for NBLM neighbourhoods. The application of the concept of neural neighbourhood to the Levenberg-Marquardt optimization method led us to the development of the NBLM algorithm. When this algorithm is used, there can be neighbourhoods that, not only produce significant reductions in memory requirements, but that also achieve better time performance than that of the Levenberg-Marquardt method. However, as long as the problem of choosing an appropriate neighbourhood size is not solved, the NBLM algorithm will not be able to offer the best possible performance.
Keywords :
learning (artificial intelligence); neural nets; optimisation; performance evaluation; Levenberg-Marquardt optimization method; NBLM neighbourhood size; memory requirements; neural neighbourhood; performance; supervised neural network training; Computational efficiency; Computational modeling; Costs; Error correction; Jacobian matrices; Neural networks; Optimization methods; Parameter estimation; Performance analysis; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
Print_ISBN :
981-04-7524-1
Type :
conf
DOI :
10.1109/ICONIP.2002.1198960
Filename :
1198960
Link To Document :
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