Finite-Time Convergent Recurrent Neural Network With a Hard-Limiting Activation Function for Constrained Optimization With Piecewise-Linear Objective Functions

This paper presents a one-layer recurrent neural network for solving a class of constrained nonsmooth optimization problems with piecewise-linear objective functions. The proposed neural network is guaranteed to be globally convergent in finite time to the optimal solutions under a mild condition on...

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Vydané v:IEEE transactions on neural networks Ročník 22; číslo 4; s. 601 - 613
Hlavní autori: Liu, Qingshan, Wang, Jun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York, NY IEEE 01.04.2011
Institute of Electrical and Electronics Engineers
Predmet:
Algorithms
Applied sciences
Artificial intelligence
Artificial neural networks
Complexity theory
Computer science; control theory; systems
Computer Simulation
Connectionism. Neural networks
Constrained optimization
Constraints
Convergence
convergence in finite time
Deviation
Exact sciences and technology
global Lyapunov method
Humans
Linear programming
Mathematical models
Neural networks
Neural Networks (Computer)
Nonlinear Dynamics
Optimization
Programming
Programming, Linear
Recurrent neural networks
Time Factors
Lower bound
Recurrent neural nets
convergence in finite time
L1 approximation
Lyapunov method
Shortest path
Linear programming
Routing
Regression analysis
Network management
Neural network
Modeling
Constrained optimization
Minimum time
recurrent neural networks
Activation function
Optimal solution
piecewise function
Objective function
global Lyapunov method
Piecewise linearization
Mathematical programming
ISSN:1045-9227, 1941-0093, 1941-0093
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Shrnutí:This paper presents a one-layer recurrent neural network for solving a class of constrained nonsmooth optimization problems with piecewise-linear objective functions. The proposed neural network is guaranteed to be globally convergent in finite time to the optimal solutions under a mild condition on a derived lower bound of a single gain parameter in the model. The number of neurons in the neural network is the same as the number of decision variables of the optimization problem. Compared with existing neural networks for optimization, the proposed neural network has a couple of salient features such as finite-time convergence and a low model complexity. Specific models for two important special cases, namely, linear programming and nonsmooth optimization, are also presented. In addition, applications to the shortest path problem and constrained least absolute deviation problem are discussed with simulation results to demonstrate the effectiveness and characteristics of the proposed neural network.
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ISSN:1045-9227
1941-0093
1941-0093
DOI:10.1109/TNN.2011.2104979