基于多模式组合推理机制的确诊方法研究  

Research on Diagnostic Method Based on Multi-mode Composite Reasoning Mechanism

作　　者：席金菊[1] 谭文学[1] 毕于通[1] 何劲舟[1] 李淑红[2] 

机构地区：[1]湖南文理学院计算机科学与技术学院,常德415000 [2]湖南文理学院生命科学学院,常德415000

出　　处：《计算机工程》2010年第11期192-194,共3页Computer Engineering

基　　金：湖南省自然科学基金资助项目(09JJ6086);湖南省"十一五"重点建设学科基金资助项目(动物学);湖南省科技计划基金资助项目(2008FJ3050);常德市科技计划基金资助项目(2007ZD10;2007ZD11);湖南文理学院大学生创新性学习基金资助项目"基于.Net山羊疾病诊断专家系统模型的研究";湖南文理学院教学改革研究课题基金资助重点项目(JGZD0608)

摘　　要：基于经典专家系统模型建立的疾病诊断系统仅单次性地推理和知识运用,知识资源利用效率低、准确度低、对比度缺失。为此,研究以确诊山羊疾病为例,引入多模式组合推理确诊机制,设计诊断系统体系架构,构造源于概率的自学习贝叶斯推理和基于模式识别的语义距离测度的不同理论背景的多模式组合推理诊断算法。实验结果表明,组合推理诊断模型提高知识库利用率,增加了对比度,准确率达到85%,取得较好的综合诊断效果。The expert system of disease diagnosis is subjected to the bad efficiency, the low accuracy and the lack of contrast, by its only one time use of the field knowledge in a reasoning process, which is built on the base of classical model. Making example of goat, this paper designs the architecture of diagnosis system, and introduces the ideology of multi-mode composite reasoning scheme, constructs both Bayesian reasoning supporting learning by self which is based on probability. The method of measuring semantic is based on pattcm recognition, and with different theoretical background. Experimental results show that the composite reasoning scheme enables to improve the utilization rate of knowledge, its accuracy of diagnosis reaches 85%, increasing the contrast, and achieves with an accepted macro effect of diagnosis.

关 键 词：组合推理 自学习式贝叶斯推理 语义距离 疾病诊断 模式识别 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]