摘要
机构轨迹综合问题本质上可以归结成为一个模式匹配问题 据此给出了机构轨迹综合的模式匹配模型 ,深入讨论了其实现步骤中涉及到的若干关键技术 ,如机构轨迹的数字化描述、轨迹曲线的聚类与分类、轨迹模式匹配等 综合运用AINE无监督学习模型、AIRS有监督学习模型和文中给出的阴性选择算法改进模型 ,提出了基于免疫计算的机构轨迹综合方法 最后 。
Mechanism path synthesis can be viewed as a pattern matching problem in essence. A pattern matching model for mechanism path synthesis is presented, and some key techniques related to its implementation such as digital description of mechanism path, clustering and classification of path curves as well as the path pattern matching are discussed in detail. Approaches of immune computing, namely the AINE model for unsupervised learning, AIRS model for supervised learning and the improved model of negative selection algorithm are exploited in an integrated way. A practical example of four-bar linkage path synthesis demonstrates that the proposed method is effective.
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2004年第6期812-818,共7页
Journal of Computer-Aided Design & Computer Graphics
基金
国家自然科学基金 ( 5 0 0 75 0 2 8)
高等学校博士点基金( 2 0 0 3 0 4870 5 4)
关键词
免疫计算
机构轨迹综合
聚类
分类
模式匹配
immune computing
mechanism path synthesis
clustering
classification
pattern matching
