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An RNN-Based Algorithm for Decentralized-Partial-Consensus Constrained Optimization
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DOI：10.1109/TNNLS.2021.3098668.png)
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This technical note proposes a decentralized-partial-consensus optimization (DPCO) problem with inequality constraints. The partial-consensus matrix originating from the Laplacian matrix is constructed to tackle the partial-consensus constraints. A continuous-time algorithm based on multiple interconnected recurrent neural networks (RNNs) is derived to solve the optimization problem. In addition, based on nonsmooth analysis and Lyapunov theory, the convergence of continuous-time algorithm is further proved. Finally, several examples demonstrate the effectiveness of main results.
Keyword:
Optimization
Recurrent neural networks
Cost function
Task analysis
Optimization methods
Neurodynamics
Learning systems
Decentralized-partial-consensus optimization (DPCO)
nonsmooth analysis
partial-consensus matrix
recurrent neural networks (RNNs)
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8.9
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7.6K
被引数:
7.2W
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