Journal
NEUROCOMPUTING
Volume 267, Issue -, Pages 107-113Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2017.05.017
Keywords
Zeroing neural network; Quadratic programming; Nonconvex function; Inequality constraint
Categories
Funding
- National Natural Science Foundation of China [61401385]
- Hong Kong Research Grants Council Early Career Scheme [25214015]
- Departmental General Research Fund of Hong Kong Polytechnic University [G.61.37.UA7L]
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Zeroing neural network (ZNN, or termed Zhang neural network after its inventor), being a special type of neurodynamic methodology, has shown powerful abilities to solve a great variety of time-varying problems with monotonically increasing odd activation functions. However, the existing results on ZNN cannot handle the inequality constraint in the optimization problem and nonconvex function cannot applied to accelerating the convergence speed of ZNN. This work breaks these limitations by proposing ZNN models, allowing nonconvex sets for projection operations in activation functions and incorporating new techniques for handing inequality constraint arising in optimizations. Theoretical analyses reveal that the proposed ZNN models are of global stability with timely convergence. Finally, illustrative simulation examples are provided and analyzed to substantiate the efficacy and superiority of the proposed ZNN models for real-time dynamic quadratic programming subject to equality and inequality constraints. (C) 2017 Elsevier B.V. All rights reserved.
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