A Universal Approximation Result for Difference of Log-Sum-Exp Neural Networks

We show that a neural network whose output is obtained as the difference of the outputs of two feedforward networks with exponential activation function in the hidden layer and logarithmic activation function in the output node, referred to as log-sum-exp (LSE) network, is a smooth universal approxi...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transaction on neural networks and learning systems Vol. 31; no. 12; pp. 5603 - 5612
Main Authors:	Calafiore, Giuseppe C., Gaubert, Stephane, Possieri, Corrado
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.12.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Artificial neural networks Computer Science Continuity (mathematics) Convex functions Data-driven optimization Design Design optimization Diabetes mellitus difference of convex (DC) programming Feedforward neural networks feedforward neural networks (FFNs) log-sum-exp (LSE) networks Machine Learning Mathematical analysis Mathematical models Mathematics Neural networks Numerical methods Optimization Optimization and Control Subtraction subtraction-free expressions surrogate models Transforms universal approximation LSE networks DC programming Subtraction-freeexpressions Surrogate models Feedforward neural networks Data-driven optimization Universal ap-proximation
ISSN:	2162-237X, 2162-2388, 2162-2388
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!