Simulation Experiment on Path Planning in Incomplete Fuzzy System Based on Dominance Rough Set

Abstract

In order to enhance the processing capacity of the inconsistent ordered study information system and transform the rough set of different precision dominance relation, they can enhance the adaptability of inconsistent information by introducing the clustering algorithm, because rough set can effectively deal with imprecise, inconsistent and incomplete information, it can effectively get rid of dependence on a priori knowledge in the learning process, and has strong ability of independent learning. Through the simulation analysis, we found that the autonomous learning method has higher inconsistency ordered information system with the prominent advantages. In the number of different rough set, this method has different accuracy and can be very good to adapt to the change of the offensive and defensive line as well as automatic planning prevention path, which provide the theory reference for the study of basketball training autonomous learning. Introduction With the growth of global economic development, competition is increasingly fierce in manufacturing industry. The core of competition has been transferred to the innovative technology and high added value of new products on the basis of competition [1,2]. In the process of new product development, in order to shorten the design cycle, improve design quality and reduce design cost, computer is introduced to the design of mechatronic simulation. Machine learning are an important field in current artificial intelligence research and the essence of machine learning is the transformation of the knowledge representation, however the data essential characteristics plays a decisive role. Because of the existence of inconsistent information in actual system, people often rely on some fields’ prior knowledge in traditional machine learning, such as probability theory, fuzzy set and other research [3-5]. How to get rid of dependence on a priori knowledge of the learning process under the uncertainty condition, independent learning is a difficult problem of the artificial intelligence knowledge acquisition in the research [6]. In this paper, using the rough set algorithm designs the autonomous learning system, and self-regulated learning system is used in the basketball training, the general framework is Figure 1. Figure 1 shows the overall design framework of rough set autonomous learning method. The use of the fuzzy semantic rules of rough set carries out the classification for data information, according to clustering algorithm we can find path planning optimization algorithm, and then we go through metadata processing tools to operate the learning database, eventually it will be applied to basketball path planning, we can get the offensive and defensive autonomous learning route. International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2015) © 2015. The authors Published by Atlantis Press 864