NDLI: A fast feedforward training algorithm using a modified form of the standard backpropagation algorithm

Content Provider	IEEE Xplore Digital Library
Author	Abid, S. Fnaiech, F. Najim, M.
Copyright Year	1990
Abstract	In this letter, a new approach for the learning process of multilayer feedforward neural network is introduced. This approach minimizes a modified form of the criterion used in the standard backpropagation algorithm. This criterion is based on the sum of the linear and the nonlinear quadratic errors of the output neuron. The quadratic linear error signal is appropriately weighted. The choice of the weighted design parameter is evaluated via rank convergence series analysis and asymptotic constant error values. The new proposed modified standard backpropagation algorithm (MBP) is first derived on a single neuron-based net and then extended to a general feedforward neural network. Simulation results of the 4-b parity checker and the circle in the square problem confirm that the performance of the MBP algorithm exceed the standard backpropagation (SBP) in the reduction of the total number of iterations and in the learning time.
Sponsorship	IEEE Computational Intelligence Society
Starting Page	424
Ending Page	430
Page Count	7
File Size	223464
File Format	PDF
ISSN	10459227
Volume Number	12
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-03-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Backpropagation algorithms Neural networks Least squares approximation Convergence Approximation algorithms Multi-layer neural network Feedforward neural networks Autoregressive processes Multilayer perceptrons Neurons
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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