Cao, GLai, EMKAlam, F2026-02-252026-02-252018-08-02International Journal of Intelligent Systems Technologies and Applications, ISSN: 1740-8865 (Print); 1740-8873 (Online), Inderscience Publishers, 17(3), 347-369.1740-88651740-8873http://hdl.handle.net/10292/20678Convolved 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.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.2018.09401946 Information and Computing Sciences4602 Artificial Intelligence0801 Artificial Intelligence and Image Processing0803 Computer Software0906 Electrical and Electronic Engineering40 Engineering46 Information and computing sciencesenhanced PSOconvolved Gaussian process modelshyperparameters learningJournal ArticleOpenAccess10.1504/IJISTA.2018.094019