  The Gaussian process (GP) model, which has been extensively applied as priors
of functions, has demonstrated excellent performance. The specification of a
large number of parameters affects the computational efficiency and the
feasibility of implementation of a control strategy. We propose a linear model
to approximate GPs; this model expands the GP model by a series of basis
functions. Several examples and simulation studies are presented to demonstrate
the advantages of the proposed method. A control strategy is provided with the
proposed linear model.
