DocumentCode
288367
Title
Dynamic adaptation of the error surface for the acceleration of the training of neural networks
Author
Thome, Antonio G. ; Tenorio, Manoel F.
Author_Institution
Parallel Process. Lab., Purdue Univ., West Lafayette, IN, USA
Volume
1
fYear
1994
fDate
27 Jun-2 Jul 1994
Firstpage
447
Abstract
Presents a technique, named ARON (adaptive region of nonlinearity), that accelerates learning processes through a dynamic adaptation of the error surface. The procedure implements a generalization of the basic McCulloch-Pitts type of neuron which gives to each unit the ability to automatically adapt its operational region according to the requirements of the problem
Keywords
learning (artificial intelligence); neural nets; optimisation; ARON; McCulloch-Pitts type neuron; adaptive region of nonlinearity; dynamic adaptation; error surface; neural networks; training acceleration; Acceleration; Convergence; Eigenvalues and eigenfunctions; Jacobian matrices; Laboratories; Neural networks; Neurons; Parallel processing; Shape; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location
Orlando, FL
Print_ISBN
0-7803-1901-X
Type
conf
DOI
10.1109/ICNN.1994.374204
Filename
374204
Link To Document