知识获取过程 meaning in English
knowledge acquisition process
Examples
- However , these researches laid emphasis on information processing ( such as transformation and operation of information in the process of the acquisition of knowledge ) , lacking studies on the regulation of learning activities
但以往这些对于学习的研究主要集中于对学习的信息加工过程本身(知识获取过程中信息的转换、操作等)的研究，而对于学习活动的控制调节方面的研究相对较少。 - Firstly , influence factors of generalization of neural network are presented in this thesis , in order to improve neural network ’ s generalization ability and dynamic knowledge acquirement adaptive ability , a structure auto - adaptive neural network new model based on genetic algorithm is proposed to optimize structure parameter of nn including hidden layer nodes , training epochs , initial weights , and so on ; secondly , through establishing integrating neural network and introducing data fusion technique , the integrality and precision of acquired knowledge is greatly improved . then aiming at the incompleteness and uncertainty problem consisting in the process of knowledge acquirement , knowledge acquirement method based on rough sets is explored to fulfill the rule extraction for intelligent diagnosis expert system , by completing missing value data and eliminating unnecessary attributes , discretization of continuous attribute , reducing redundancy , extracting rules in this thesis . finally , rough sets theory and neural network are combined to form rnn ( rough neural network ) model for acquiring knowledge , in which rough sets theory is employed to carry out some preprocessing and neural network is acted as one role of dynamic knowledge acquirement , and rnn can improve the speed and quality of knowledge acquirement greatly
本文首先讨论了影响神经网络的泛化能力的因素,提出了一种新的结构自适应神经网络学习算法,在新方法中,采用了遗传算法对神经网络的结构参数(隐层节点数、训练精度、初始权值)进行优化,大大提高了神经网络的泛化能力和知识动态获取自适应能力;其次,构造集成神经网络,引入数据融合算法,实现了基于集成神经网络的融合诊断,有效地提高了知识获取的全面性、完善性及精度;然后,针对知识获取过程中所存在的不确定性、不完备性等问题,探讨了运用粗糙集理论的知识获取方法,通过缺损数据补齐、连续数据的离散、冲突消除、冗余信息约简、知识规则抽取等一系列的算法实现了智能诊断的知识规则获取;最后,将粗糙集理论与神经网络相结合,研究了粗糙集-神经网络的知识获取方法。