Optimization Design of Track Control Algorithm Based on Convolutional Neural Network

Abstract

At present, the track control of unmanned surface vessel (USV) is mainly divided into direct track control and indirect track control, both of which have their advantages in the aspect of USV track control. Though having high control precision, the former still fails to meet the related requirements. To tackle this problem, the nonlinear model predictive control (NMPC) algorithm was optimized based on the convolutional neural network (CNN) in order to design a direct USV track controller. In consideration of the nonlinearity and time lag problems that are likely to occur in USV track control, the convolutional neural network was used to solve the optimal control sequence of the predictive control algorithm. The simulation test indicates that this algorithm has effectively improved the accuracy and real-time performance of track control.

Application of Art Activities in Colleges and Universities Based on BP Neural Network Algorithm

“…At present, in the teaching of basic courses of art education in colleges and universities, due to the imbalance between teaching resources and the number of students, the number of classes is large, and the number of teachers is limited. Teachers cannot teach students in accordance with their aptitude, and the teaching effect often fails to achieve the goal, resulting in the monotonous style of students' creative works and the lack of their own ideas [6,7]. Due to the limited time, energy, and ability, teachers cannot teach in a targeted manner according to the characteristics of each student [8].…”