Paper Info
Title
Enhanced Particle Swarm Optimization Algorithms for Multiple-Input Multiple-Output System Modelling using Convolved Gaussian Process Models.
Abstract
Convolved Gaussian process (CGP) can capture the input-output correlation, and the correlation of multiple outputs. This is beneficial to the modelling problem of multiple-input multiple-output (MIMO) systems. One key issue of CGP is the learning of hyperparameters from input-output observations. This is typically performed by maximising the log-likelihood (LL) function using gradient based approaches. However, the LL value is not a reliable indicator for judging the quality of intermediate models. We address this issue by minimising the model output error instead. In addition, three enhanced particle swarm optimisation (PSO) algorithms are proposed to solve the optimisation problem because gradient based approaches often get stuck in local optima. The simulation results on numerical linear and nonlinear systems demonstrate the effectiveness of minimising the model output error to learn hyperparameters, and the better performance of using enhanced PSOs compared to gradient based approaches.
Year
DOI
Venue
2018
10.1504/ijista.2018.10015235
IJISTA
DocType
Volume
Issue
Journal
abs/1709.04319
3
Citations 
PageRank 
References 
0
0.34
6
Authors
3
Name
Order
Citations
PageRank
Gang Cao11137.11
Edmund Ming-Kit Lai212058.89
Fakhrul Alam3209.06