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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Veröffentlicht in:	IEEE transactions on neural networks Jg. 22; H. 4; S. 601 - 613
Hauptverfasser:	Liu, Qingshan, Wang, Jun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.04.2011 Institute of Electrical and Electronics Engineers
Schlagworte:	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
Online-Zugang:	Volltext
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