Sparse Bayesian Learning Based on Collaborative Neurodynamic Optimization
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 13669-13683 |
Journal / Publication | IEEE Transactions on Cybernetics |
Volume | 52 |
Issue number | 12 |
Online published | 14 Jul 2021 |
Publication status | Published - Dec 2022 |
Link(s)
Abstract
Regression in a sparse Bayesian learning (SBL) framework is usually formulated as a global optimization problem with a nonconvex objective function and solved in a majorization-minimization framework where the solution quality and consistency depend heavily on the initial values of the used algorithm. In view of the shortcomings, this article presents an SBL algorithm based on collaborative neurodynamic optimization (CNO) for searching global optimal solutions to the global optimization problem. The CNO system consists of a population of recurrent neural networks (RNNs) where each RNN is convergent to a local optimum to the global optimization problem. Reinitialized repetitively via particle swarm optimization with exchanged local optima information, the RNNs iteratively improve their searching performance until reaching global convergence. The proposed CNO-based SBL algorithm is almost surely convergent to a global optimal solution to the formulated global optimization problem. Two applications with experimental results on sparse signal reconstruction and partial differential equation identification are elaborated to substantiate the superiority and efficacy of the proposed method in terms of solution optimality and consistency.
Research Area(s)
- Bayes methods, Collaborative neurodynamic optimization (CNO), global optimization, Inference algorithms, Linear programming, Mathematical model, Neurodynamics, Optimization, Recurrent neural networks, regression, sparse Bayesian learning (SBL)
Citation Format(s)
Sparse Bayesian Learning Based on Collaborative Neurodynamic Optimization. / Zhou, Wei; Zhang, Hai-Tao; Wang, Jun.
In: IEEE Transactions on Cybernetics, Vol. 52, No. 12, 12.2022, p. 13669-13683.
In: IEEE Transactions on Cybernetics, Vol. 52, No. 12, 12.2022, p. 13669-13683.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review