Enhanced Particle Swarm Optimisation Algorithms for Multiple-input Multiple-output System Modelling Using Convolved Gaussian Process Models
Date
Authors
Cao, G
Lai, EMK
Alam, F
Supervisor
Item type
Journal Article
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
Inderscience Publishers
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.Description
Keywords
46 Information and Computing Sciences, 4602 Artificial Intelligence, 0801 Artificial Intelligence and Image Processing, 0803 Computer Software, 0906 Electrical and Electronic Engineering, 40 Engineering, 46 Information and computing sciences, enhanced PSO, convolved Gaussian process models, hyperparameters learning
Source
International Journal of Intelligent Systems Technologies and Applications, ISSN: 1740-8865 (Print); 1740-8873 (Online), Inderscience Publishers, 17(3), 347-369.
Publisher's version
Rights statement
This is the Author's Accepted Manuscript of an article published in the International Journal of Intelligent Systems Technologies and Applications © 2018 Inderscience Enterprises Ltd. The Version of Record can be found at DOI: 10.1504/IJISTA.2018.094019