NDLI: An Approach to Syndrome Differentiation in Traditional Chinese Medicine based on Neural Network

Content Provider	IEEE Xplore Digital Library
Author	Minghui Shi Changle Zhou
Copyright Year	2007
Description	Author affiliation: Xiamen Univ., Xiamen (Minghui Shi; Changle Zhou)
Abstract	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.
Starting Page	376
Ending Page	380
File Size	238684
Page Count	5
File Format	PDF
ISBN	9780769528755
DOI	10.1109/ICNC.2007.182
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy sets Neural networks Production Artificial neural networks Knowledge representation Back Inference algorithms Artificial intelligence Diseases Multi-layer neural network
Content Type	Text
Resource Type	Article

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