NDLI: Modified backpropagation algorithms for training the multilayer feedforward neural networks with hard-limiting neurons

Content Provider	IEEE Xplore Digital Library
Author	Xiangui Yu Loh, N.K. Jullien, G.A. Miller, W.C.
Copyright Year	1994
Description	Author affiliation: Dept. of Electr. Eng., Windsor Univ., Ont., Canada (Xiangui Yu)
Abstract	This paper introduces modified backpropagation algorithms for training multilayer feedforward neural networks with hard-limiting neurons. Transforming neuron activation functions are used in all the hidden layers, which are the modified continuous sigmoidal functions with an adaptive steepness factors. In the training process, this steepness factor varies from a small positive value to infinite with the decrease of the sum square error. Thus, a multilayer feedforward neural network can be trained with the resultant architecture only composed of hard limiting neurons. The learning algorithm is similar to the conventional backpropagation algorithm. Only the derivatives of the hidden neural activation functions are modified. Extensive numerical simulations are presented to show the feasibility of the proposed algorithm. In addition, the numerical properties of the proposed algorithm are discussed and comparisons of the proposed algorithm with other algorithms are made.<>
Starting Page	473
Ending Page	476
File Size	394758
Page Count	4
File Format	PDF
ISBN	0780324161
DOI	10.1109/CCECE.1993.332191
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1993-09-14
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Multi-layer neural network Backpropagation algorithms Neural networks Feedforward neural networks Neurons Multilayer perceptrons Very large scale integration Robotics and automation Electronic mail Numerical simulation
Content Type	Text
Resource Type	Article

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