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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Vydáno v:IEEE transactions on neural networks Ročník 19; číslo 4; s. 558 - 570
Hlavní autoři: Liu, Q, Wang, J
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York, NY IEEE 01.04.2008
Institute of Electrical and Electronics Engineers
Témata:
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.
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ISSN:1045-9227
1941-0093
DOI:10.1109/TNN.2007.910736