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...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 31; H. 12; S. 5603 - 5612
Hauptverfasser:	Calafiore, Giuseppe C., Gaubert, Stephane, Possieri, Corrado
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.12.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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-Zugang:	Volltext
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