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In this paper, a global asymptotic stability condition for Long Short-Term Memory neural networks is presented. Since this condition is formulated in terms of the networks’ weight matrices and biases… Click to show full abstract
In this paper, a global asymptotic stability condition for Long Short-Term Memory neural networks is presented. Since this condition is formulated in terms of the networks’ weight matrices and biases that are essentially control variables, the same condition can be viewed as a way to globally asymptotically stabilize these networks. The condition and how to compute numerical values for the weight matrices and biases are illustrated by a number of numerical examples.
Keywords:
term memory;
memory neural;
long short;
asymptotic stability;
global asymptotic;
short term
Journal Title: Journal of Optimization Theory and Applications
Year Published: 2019
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Journal Title: Journal of Optimization Theory and Applications
Year Published: 2019
Link to full text (if available)
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recommendations!