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
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Popis
Shrnutí: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 function is convex on the set defined by equality constraints. Compared with existing one-layer neural networks for quadratic programming problems, the proposed neural network has the least neurons and requires weak stability conditions. The validity and transient behavior of the proposed neural network are demonstrated by some simulation results.
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ISSN:1045-9227
1941-0093
1941-0093
DOI:10.1109/TNN.2010.2045129