Title: Enhanced particle swarm optimisation algorithms for multiple-input multiple-output system modelling using convolved Gaussian process models

Authors: Gang Cao; Edmund M-K. Lai; Fakhrul Alam

Addresses: School of Engineering and Advanced Technology, Massey University, Auckland, 0632, New Zealand ' Department of Information Technology and Software Engineering, Auckland University of Technology, Auckland, 0632, New Zealand ' School of Engineering and Advanced Technology, Massey University, Auckland, 1010, New Zealand

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.

Keywords: enhanced PSO; convolved Gaussian process models; hyperparameters learning.

DOI: 10.1504/IJISTA.2018.094019

International Journal of Intelligent Systems Technologies and Applications, 2018 Vol.17 No.3, pp.347 - 369

Received: 01 Dec 2016
Accepted: 18 Jul 2017
Published online: 13 Aug 2018 *

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