主观贝叶斯自学习推理动物疾病确诊方法研究

Research on sheep disease diagnostic expert system based on subjective Bayes algorithm with self-learning

基于传统专家系统技术建立的疾病诊断系统大多数仅仅运用历史知识进行推理,缺乏学习能力,致使知识更新效率低,结论准确度不高.研究以确诊山羊疾病为例,提出主观自学习贝叶斯推理确诊机制,设计专家系统体系架构,构造贝叶斯推理方法和学习算法.实验表明:同传统方法相比,该诊断求解模型提高了知识库利用率,改善了准确度,增加了学习智能,获得满意的实验效果.图1,表2,参8.

The diagnosis majority of systems which based on the tradition of expert system technology perform reasoning only by historical knowledge, lack of ability to learn, which contributing to low efficiency of knowledge studying, the low accuracy of conclusion. This study taking example of sheep Disease diagnosis, constructed Bayes reasoning supporting learning by self, designed the architecture of expert system,. Experimental results showed： model of Bayes supporting learr^ing by self reasoning scheme enables to improve the utilization rate of knowledge, successfully^improve the accuracy of diagnosis, increase the intelligence of learning, harvest a good Experimental effect.