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
fDate :
27 Jun-2 Jul 1994
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;
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
DOI :
10.1109/ICNN.1994.374204
