Learning algorithms for perceptions using back-propagation with selective updates
- 1 April 1990
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Control Systems Magazine
- Vol. 10 (3) , 56-61
- https://doi.org/10.1109/37.55125
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
This publication has 14 references indexed in Scilit:
- Approximation by superpositions of a sigmoidal functionMathematics of Control, Signals, and Systems, 1989
- Toward intelligent controlIEEE Control Systems Magazine, 1989
- Towards intelligent autonomous control systems: Architecture and fundamental issuesJournal of Intelligent & Robotic Systems, 1989
- Mapping abilities of three-layer neural networksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1989
- Random problemsJournal of Complexity, 1988
- Neurocomputing: picking the human brainIEEE Spectrum, 1988
- An introduction to neural computingNeural Networks, 1988
- An introduction to computing with neural netsIEEE ASSP Magazine, 1987
- VLSI architectures for implementation of neural networksAIP Conference Proceedings, 1986
- Neuronlike adaptive elements that can solve difficult learning control problemsIEEE Transactions on Systems, Man, and Cybernetics, 1983