A New One-Layer Neural Network for Linear and Quadratic Programming

In this paper, we present a new neural network for solving linear and quadratic programming problems in real time by introducing some new vectors. The proposed neural network is stable in the sense of Lyapunov and can converge to an exact optimal solution of the original problem when the objective f...

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Vydáno v:	IEEE transactions on neural networks Ročník 21; číslo 6; s. 918 - 929
Hlavní autoři:	XINGBAO GAO, LIAO, Li-Zhi
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York, NY IEEE 01.06.2010 Institute of Electrical and Electronics Engineers
Témata:	Applied sciences Artificial intelligence Artificial neural networks Computer networks Computer science; control theory; systems Computer Simulation Connectionism. Neural networks Convergence Design optimization Exact sciences and technology Humans linear and quadratic programming Linear programming Mathematical analysis Mathematical programming Mathematics neural network Neural networks Neural Networks (Computer) Neurons Nonlinear Dynamics Operational research and scientific management Operational research. Management science Optimization Programming, Linear Quadratic programming Real time Stability Vectors Vectors (mathematics) Transient response linear and quadratic programming Lyapunov method Linear programming Neural network Quadratic programming Real time Modeling Transients Exact solution Convergence Neuron Optimal solution Equality constraint Convex function Objective function stability Lyapunov function
ISSN:	1045-9227, 1941-0093, 1941-0093
On-line přístup:	Získat plný text
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