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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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 31; H. 6; S. 2020 - 2029
Hauptverfasser:	Xia, Youshen, Wang, Jun, Guo, Wenzhong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.06.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
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