A multi-output
Gaussian process regression
underwater vehicle
system identification
algorithm based on a depth part auto-
encoder comprises the following steps: designing an amplitude-adjustable zigzag vertical
rudder input
signal, obtaining state data of an
underwater vehicle, and dividing the state data into a
training set and a
verification set; constructing an autoregression model based on
underwater vehicle dynamics according to the state data; building a depth part auto-
encoder according to the auto-regression model; low-dimensional
feature data extracted by the depth part auto-
encoder is used as input of a multi-output
Gaussian process, output of the auto-regression model is used as output of the multi-output
Gaussian process, hyper-parameters of the multi-output
Gaussian process are searched through a
particle swarm algorithm, and model training is carried out; when a model training error reaches preset precision, completing model training, and verifying the model by adopting
verification set data; the problems that high-dimensional input of the multi-output
Gaussian process can increase calculation of a kernel function and search time of hyper-parameters, the complexity of a model is increased, and prediction efficiency of the multi-output
Gaussian process is reduced are solved.