Abstract:

The Gaussian process (GP) is a high and new machine learning way developed rapidly in recent years. It embodies the characteristics of programming easily, self-adaptive acquisition of hyper-parameters and prediction with probability interpretation which are superior to those of SVM. Aiming at the slope angle design influenced by many factors in slope engineering, the Gaussian process with the combinatorial kernel function obtained by combination of squared exponential and rational quadratic covariance function is implemented by learning machine routine in Matlab for overcoming poor predictive precision and network generalization ability of single kernel function. Then the automatic relevant determination (ARD) is introduced into combinatorial Gaussian kernel function in the programme and a GP regression model with regard to hyper-parameters is established; meanwhile the correlation and characteristics selection about inputs and prediction for testing samples on the basis of the net are completed respectively. The results show that compared with support vector regression method, the prediction precision of GP is not only slightly better about all error indexes; and the prediction results reflect well uniformity of predictive precision and dispersion between predictive value and measured value, but also the physical meaning of ARD parameters are distinct; so it can be served as a new tool in slope angle design.

Key words: slope engineering, Gaussian process, slope angle design, machine learning, intelligent prediction