Title :
A One-Layer Recurrent Neural Network for Pseudoconvex Optimization Subject to Linear Equality Constraints
Author :
Guo, Zhishan ; Liu, Qingshan ; Wang, Jun
Author_Institution :
Dept. of Comput. Sci., Univ. of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Abstract :
In this paper, a one-layer recurrent neural network is presented for solving pseudoconvex optimization problems subject to linear equality constraints. The global convergence of the neural network can be guaranteed even though the objective function is pseudoconvex. The finite-time state convergence to the feasible region defined by the equality constraints is also proved. In addition, global exponential convergence is proved when the objective function is strongly pseudoconvex on the feasible region. Simulation results on illustrative examples and application on chemical process data reconciliation are provided to demonstrate the effectiveness and characteristics of the neural network.
Keywords :
convex programming; recurrent neural nets; chemical process data reconciliation; finite-time state convergence; global convergence; global exponential convergence; linear equality constraint; one-layer recurrent neural network; pseudoconvex optimization; Convergence; Convex functions; Optimization; Recurrent neural networks; Transient analysis; Vectors; Global convergence; linear equality constraints; pseudoconvex optimization; recurrent neural networks; Algorithms; Computer Simulation; Linear Models; Neural Networks (Computer);
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2011.2169682
