Grid index subspace constructed locally weighted learning identification modeling for high dimensional ship maneuvering system

Abstract For off-line locally weighted learning (LWL), all training data points need to be stored in memory, which would lead to a heavy computational burden, especially for large amount of training data. To avoid heavy computational burden in LWL, the grid index subspace constructed algorithm is presented for high dimensional ship maneuvering system in this study. First, high dimensional training data can be encoded and stored in equal interval grid, and training data are divided into grids. Second, query point is encoded by using the same strategy as in the first step, and the grid number which belongs to the query point is obtained. Third, the subspace would be per-allocated to the query point by using the grid index which has a light computational complexity. Different from the general cluster algorithm, a subspace rather than a neighborhood is assigned to query point. This way, LWL is carried out in a subspace, and the computational complexity is significantly reduced. As a consequence, real-time performance is effectively guaranteed. Finally, theoretical calculations and simulation examples are given to validate the effectiveness of the proposed scheme.