A One-Layer Recurrent Neural Network With a Discontinuous Hard-Limiting Activation Function for Quadratic Programming

In this paper, a one-layer recurrent neural network with a discontinuous hard-limiting activation function is proposed for quadratic programming. This neural network is capable of solving a large class of quadratic programming problems. The state variables of the neural network are proven to be glob...

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Veröffentlicht in:	IEEE transactions on neural networks Jg. 19; H. 4; S. 558 - 570
Hauptverfasser:	Liu, Q, Wang, J
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.04.2008 Institute of Electrical and Electronics Engineers
Schlagworte:	Activation Applied sciences Artificial intelligence Computer science; control theory; systems Computer Simulation Connectionism. Neural networks Convergence Data processing. List processing. Character string processing Differential inclusion Exact sciences and technology global convergence hard-limiting activation function Lagrangian functions Linear programming Lyapunov stability Machine learning Mathematical analysis Mathematical models Memory organisation. Data processing Nerve Net Neural network hardware Neural networks Nonlinear Dynamics nonlinear programming Numerical simulation Programming, Linear Quadratic programming recurrent neural network Recurrent neural networks Signal Processing, Computer-Assisted Software Support vector machines Time Factors Recurrent neural nets Statistical analysis recurrent neural network nonlinear programming global convergence Lyapunov method Non linear programming Neural network Quadratic programming hard-limiting activation function Activation function Sequential method Optimal solution Equality constraint Vector support machine Lyapunov stability Convex function Objective function Differential inclusion Lyapunov function Quadratic function
ISSN:	1045-9227, 1941-0093
Online-Zugang:	Volltext
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