NDLI: A Modified Learning Algorithm for Interval Perceptrons with Interval Weights

Content Provider	Springer Nature Link
Author	Yang, Dakun Li, Zhengxue Liu, Yan Zhang, Huisheng Wu, Wei
Copyright Year	2014
Abstract	In many applications, it is natural to use interval data to describe various kinds of uncertainties. This paper is concerned with a one-layer interval perceptron with the weights and the outputs being intervals and the inputs being real numbers. In the original learning method for this interval perceptron, an absolute value function is applied for newly learned radii of the interval weights, so as to force the radii to be positive. This approach seems unnatural somehow, and might cause oscillation in the learning procedure as indicated in our numerical experiments. In this paper, a modified learning method is proposed for this one-layer interval perceptron. We do not use the function of the absolute value, and instead, we replace, in the error function, the radius of each interval weight by a quadratic term. This simple trick does not cause any additional computational work for the learning procedure, but it brings about the following three advantages: First, the radii of the intervals of the weights are guaranteed to be positive during the learning procedure without the help of the absolute value function. Secondly, the oscillation mentioned above is eliminated and the convergence of the learning procedure is improved, as indicated by our numerical experiments. Finally, a by-product is that the convergence analysis of the learning procedure is now an easy job, while the analysis for the original learning method is at least difficult, if not impossible, due to the non-smoothness of the absolute value function involved.
Starting Page	381
Ending Page	396
Page Count	16
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	42
Issue Number	2
e-ISSN	1573773X
Language	English
Publisher	Springer US
Publisher Date	2014-05-29
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Interval perceptron Interval computation Interval neural network Interval weight Convergence Artificial Intelligence (incl. Robotics) Statistical Physics, Dynamical Systems and Complexity Computational Intelligence
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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