Particle swarm optimization for convolved Gaussian process models

Convolved Gaussian process (CGP) is a type Gaussian process modelling technique applicable for multiple-input multiple-output systems. It employs convolution processes to construct a covariance function that models the correlation between outputs. Modelling using CGP involves learning the hyperparameters of the latent function and the smoothing kernel. Conventionally, learning involves the maximization of the log likelihood function of the training samples using conjugate gradient (CG) or particle swarm optimization (PSO) methods. We propose to use PSO to minimize the model error. In this way, a clearer direct indication of the quality of the current solution during the optimization process can be obtained. Simulation results on a dynamical system show that our method is able to learn appropriate CGP models and achieve better predictive performance compared with CG when the searching space is not well defined.