Adaptive boost (AdaBoost) is a prominent example of an ensemble learning algorithm that combines weak classifiers into strong classifiers through weighted majority voting rules. AdaBoost’s weak classifier, with threshold classification, tries to find the best threshold in one of the data dimensions, dividing the data into two categories-1 and 1. However, in some cases, this Weak Learning algorithm is not accurate enough, showing poor generalization performance and a tendency to over-fit. To solve these challenges, we first propose a new Weak Learning algorithm that classifies examples based on multiple thresholds, rather than only one, to improve its accuracy. Second, in this paper, we make changes to the weight allocation scheme of the Weak Learning algorithm based on the AdaBoost algorithm to use potential values of other dimensions in the classification process, while the theoretical identification is provided to show its generality. Finally, comparative experiments between the two algorithms on 18 datasets on UCI show that our improved AdaBoost algorithm has a better generalization effect in the test set during the training iteration.

The compound control of active-disturbance-rejection control (ADRC) with sliding mode is proposed to improve the performance of the closed-loop system and deal with the constraint condition problem of a surface ship. The advantages of ADRC with sliding mode were verified by ship course control simulations. Meanwhile, to solve the path-following problem of underactuated surface ships with uncertainties of internal dynamic and external disturbances, the ADRC controller with sliding mode is introduced to steer the ship to follow the desired path. In order to overcome the cross-track error caused by wind and current, drift angle is compensated in the controller by designing a coordinate transformation equation. Simulations were performed on a nonlinear kinematics model of a training ship to validate the stability and excellent robustness of the proposed path-following controller.