title: A one-layer recurrent neural network for pseudoconvex optimization subject to linear equality constraints.
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.
authors: Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. zsguo@cs.unc.edu
authors: Zhishan
authors: Z
authors: Guo
authors: Qingshan
authors: Q
authors: Liu
authors: Jun
authors: J
authors: Wang
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D016014:Linear Models; D016571:Neural Networks, Computer