Two Projection Neural Networks With Reduced Model Complexity for Nonlinear Programming

Recent reports show that projection neural networks with a low-dimensional state space can enhance computation speed obviously. This paper proposes two projection neural networks with reduced model dimension and complexity (RDPNNs) for solving nonlinear programming (NP) problems. Compared with exist...

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Vydáno v:	IEEE transaction on neural networks and learning systems Ročník 31; číslo 6; s. 2020 - 2029
Hlavní autoři:	Xia, Youshen, Wang, Jun, Guo, Wenzhong
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.06.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Complexity Computation Computational complexity Concave programming Convex programming fast computation global stability Hessian matrices Lagrangian function low-dimensional state space Mathematical analysis Matrix methods Neural networks nonconvex programming Nonlinear programming Optimization Projection Quadratic programming
ISSN:	2162-237X, 2162-2388, 2162-2388
On-line přístup:	Získat plný text
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