Title :
Learning algorithms for perceptions using back-propagation with selective updates
Author :
Huang, Shih-Chi ; Huang, Yih-Fang
Author_Institution :
Dept. of Electr. & Comput. Eng., Notre Dame Univ., IN, USA
fDate :
4/1/1990 12:00:00 AM
Abstract :
The error back-propagation algorithm for perceptrons is studied, and an extension of this algorithm that features selective learning is introduced. In selective learning, one of two selection criteria is used to screen the input data to improve the convergence property of the back-propagation algorithm. An associative content addressable memory using multilayer perceptrons is devised to demonstrate the improver convergence.<>
Keywords :
artificial intelligence; content-addressable storage; learning systems; associative content addressable memory; back-propagation; convergence; learning algorithms; perceptions; Artificial neural networks; Control systems; Convergence; Integrated circuit interconnections; Multilayer perceptrons; Neural networks; Neurons; Nonlinear control systems; Supervised learning; Very large scale integration;
Journal_Title :
Control Systems Magazine, IEEE
