A One-Layer Recurrent Neural Network for Pseudoconvex Optimization Subject to Linear Equality Constraints
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.
Z. Guo et al., "A One-Layer Recurrent Neural Network for Pseudoconvex Optimization Subject to Linear Equality Constraints," IEEE Transactions on Neural Networks, vol. 22, no. 12 Part 1, Institute of Electrical and Electronics Engineers (IEEE), Dec 2011.
The definitive version is available at https://doi.org/10.1109/TNN.2011.2169682
Keywords and Phrases
Global convergence; Linear equality constraints; Pseudoconvex optimization; recurrent neural networks
International Standard Serial Number (ISSN)
Article - Journal
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