Multi-dimensional Local Weighted Regression Ship Motion Identification Modeling Based on Particle Swarm optimization

Abstract

By improving the strategy of distance measure learning in traditional local weighted algorithm, a local weighted ridge regression algorithm based on particle swarm optimization (PSO) is proposed. Different from the previous strategies of global spatial optimal based distance measure learning, the different distance measures in different dimensions are learned by PSO, which improves the accuracy and adaptability of the algorithm. For the problem that the local weighted learning initial value selection can only rely on the experience, the idea of PSO is introduced to avoid the difficulty of selecting the initial value. Meanwhile, by using black-box identification modeling based on ridge regression, the multicollinearity problem and the unmodeled dynamic problem in the ship maneuvering motion modeling are solved. Through learning of 3-DOF mariner ship model, the effectiveness and generalization of the algorithm are verified, and the modeling of the nonlinear system is realized.