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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Vydané v:	IEEE transactions on neural networks Ročník 19; číslo 4; s. 558 - 570
Hlavní autori:	Liu, Q, Wang, J
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York, NY IEEE 01.04.2008 Institute of Electrical and Electronics Engineers
Predmet:	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
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Popis
Shrnutí:	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 globally stable and the output variables are proven to be convergent to optimal solutions as long as the objective function is strictly convex on a set defined by the equality constraints. In addition, a sequential quadratic programming approach based on the proposed recurrent neural network is developed for general nonlinear programming. Simulation results on numerical examples and support vector machine (SVM) learning show the effectiveness and performance of the neural network.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1045-9227 1941-0093
DOI:	10.1109/TNN.2007.910736