QQ客服 官方微博 反馈留言

	An approach to syndrome differentiation in traditional chinese medicine based on neural network
	Shi, Minghui ; Zhou, Changle
	2007
英文摘要	Although the traditional knowledge representation based on rules is simple and explicit, it is not effective in the field of syndrome differentiation in Traditional Chinese Medicine (TCM), which involves many uncertain concepts. To represent uncertain knowledge of syndrome differentiation in TCM, two methods were presented respectively based on certainty factors and certainty intervals. Exploiting these two methods, an approach to syndrome differentiation in TCM was proposed based on neural networks to avoid some limitations of other approaches. The main advantage of the approach is that it may realize uncertain inference of syndrome differentiation in TCM, whereas it doesn't request experts to provide all possible combinations for certainty degrees of symptoms and syndromes. Rather than Back Propagation (BP) algorithm but its modification was employed to improve the capability of generalization of neural networks. First, the standard feedforward multilayer BP neural network and its modification were introduced. Next, two methods for knowledge representation, respectively based on certainty factors and certainty intervals, were presented Then, the algorithm was proposed based on neural network for the uncertain inference of syndrome differentiation in TCM. Finally, an example was demonstrated to illustrate the algorithm.
语种	英语
内容类型	期刊论文
源URL	[http://dspace.xmu.edu.cn/handle/2288/70810]
专题	信息技术－已发表论文
推荐引用方式 GB/T 7714	Shi, Minghui,Zhou, Changle. An approach to syndrome differentiation in traditional chinese medicine based on neural network[J],2007.
APA	Shi, Minghui,&Zhou, Changle.(2007).An approach to syndrome differentiation in traditional chinese medicine based on neural network..
MLA	Shi, Minghui,et al."An approach to syndrome differentiation in traditional chinese medicine based on neural network".(2007).

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。