Sparse Bayesian Learning Based on Collaborative Neurodynamic Optimization

Wei Zhou

doi:10.1109/TCYB.2021.3090204

Return

Sparse Bayesian Learning Based on Collaborative Neurodynamic Optimization

2022-12-01

PRE

Wei Zhou

Hai‐Tao Zhang *

王娟 (Jun Wang) *

DOI：10.1109/TCYB.2021.3090204

Original

Original request for help

Save

Abstract

En 中文

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.

Keywords:

Optimization

Bayes methods

Neurodynamics

Recurrent neural networks

Mathematical model

Linear programming

Inference algorithms

Collaborative neurodynamic optimization (CNO)

global optimization

regression

sparse Bayesian learning (SBL)