Two-Timescale Multilayer Recurrent Neural Networks for Nonlinear Programming

This article presents a neurodynamic approach to nonlinear programming. Motivated by the idea of sequential quadratic programming, a class of two-timescale multilayer recurrent neural networks is presented with neuronal dynamics in their output layer operating at a bigger timescale than in their hid...

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Vydáno v:IEEE transaction on neural networks and learning systems Ročník 33; číslo 1; s. 37 - 47
Hlavní autoři: Wang, Jiasen, Wang, Jun
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Global optimization
Mathematical model
Multilayers
Neural networks
Neurodynamic optimization
Neurodynamics
Nonhomogeneous media
Nonlinear programming
Programming
Quadratic programming
Recurrent neural networks
Time
two-timescale system
ISSN:2162-237X, 2162-2388, 2162-2388
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Shrnutí:This article presents a neurodynamic approach to nonlinear programming. Motivated by the idea of sequential quadratic programming, a class of two-timescale multilayer recurrent neural networks is presented with neuronal dynamics in their output layer operating at a bigger timescale than in their hidden layers. In the two-timescale multilayer recurrent neural networks, the transient states in the hidden layer(s) undergo faster dynamics than those in the output layer. Sufficient conditions are derived on the convergence of the two-timescale multilayer recurrent neural networks to local optima of nonlinear programming problems. Simulation results of collaborative neurodynamic optimization based on the two-timescale neurodynamic approach on global optimization problems with nonconvex objective functions or constraints are discussed to substantiate the efficacy of the two-timescale neurodynamic approach.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2020.3027471