摘要
本文基于非线性动力学,特别是托姆的形态发生学思想,针对视觉学习,给出了一种吸引分岔知识网模型,用于解决知识表示和获取问题.通过引入皮亚杰发生认识论中的概念,模型拥有的学习功能包括强化、同化、顺应、聚合、分裂和遗忘;这样就给出了一个学习视觉知识的完整方法.3个应用系统的结果表明,该模型及其学习方法,对于解决实际问题,是有效和适用的.
Based on the idea of the nonlinear dynamics,especially the Thom's Morphogenesis,a knowledge model,called attracting-bifurcating net,is advanced to solve the problem of knowledge representation and acquisition for visual learning in this paper.By introducing the Piaget's concepts in genetic epistemology,the model possesses seven main functions which include consolidatation,assimilation,reunion,accommodation,segmentation and forgetting, thus having given out a completed method for learning visual knowledge.The results three application systems show that the model arid its learning method are effective and adaptable to solve visual problems.
出处
《软件学报》
EI
CSCD
北大核心
1996年第8期505-512,共8页
Journal of Software
基金
浙江省自然科学基金
关键词
知识表示
知识获取
吸引分岔网
人工智能
Knowledge representation
knowledge acquisition
learning method
visual computation model
attraction-bifuracting net.