| 基于模糊规则提升理论的马病辅助诊断专家系统 |
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| 引用本文： | 秦宏宇,李建新,高翔,王欢,肖建华,王洪斌.基于模糊规则提升理论的马病辅助诊断专家系统[J].农业工程学报,2016,32(5):194-199. |
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| 作者姓名： | 秦宏宇 李建新 高翔 王欢 肖建华 王洪斌 |
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| 作者单位： | 东北农业大学动物医学学院,哈尔滨,150030 |
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| 基金项目： | 十二五农村领域国家科技计划课题（2012BAD46B04-01） |
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| 摘 要： | 为解决中国马产业发展过程中马病兽医专家严重缺乏的问题,该文在系统地挖掘马病专家临床经验与诊断思维的基础上,全面改进了传统的专家系统推理机制与知识表示方法,采用对象-属性-值三元组法(object-attribute-value,O-A-V三元组法)对马病知识进行表示,应用置信系数多值逻辑对知识模糊性进行评价,并在系统中集成模糊规则提升理论(fuzzy rule promotion theory),利用该机制不断调整提升置信系数(promote confidence factor,PCF),进而实现规则置信度的动态、实时调整与优化。最终采用Microsoft.Net操作平台,SQL Server 2008数据库管理工具,研制开发了基于B/S结构的马病辅助诊断专家系统。结果应用临床上已经确诊的大量病例对系统的规则置信度进行动态调整,通过13次样本病例测试,系统诊断符合率由原来的56.47%提高并维持在92.28%。结果表明,该文采用的置信系数多值逻辑知识评价方法与模糊规则提升理论可显著提高系统诊断准确率,为马病辅助诊断专家系统的临床应用奠定了基础,同时也为开发其他动物疾病的诊断专家系统提供了新的思路。
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| 关 键 词： | 疾病 诊断 知识表示 模糊规则 规则提升 马 |
| 收稿时间： | 2015/11/4 0:00:00 |
| 修稿时间： | 1/8/2016 12:00:00 AM |
Equine diseases auxiliary diagnosis expert system based on fuzzy rule promotion theory |
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| Qin Hongyu,Li Jianxin,Gao Xiang,Wang Huan,Xiao Jianhua and Wang Hongbin.Equine diseases auxiliary diagnosis expert system based on fuzzy rule promotion theory[J].Transactions of the Chinese Society of Agricultural Engineering,2016,32(5):194-199. |
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| Authors: | Qin Hongyu Li Jianxin Gao Xiang Wang Huan Xiao Jianhua and Wang Hongbin |
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| Institution: | College of Veterinary Medicine, Northeast Agricultural University, Harbin 150030, China,College of Veterinary Medicine, Northeast Agricultural University, Harbin 150030, China,College of Veterinary Medicine, Northeast Agricultural University, Harbin 150030, China,College of Veterinary Medicine, Northeast Agricultural University, Harbin 150030, China,College of Veterinary Medicine, Northeast Agricultural University, Harbin 150030, China and College of Veterinary Medicine, Northeast Agricultural University, Harbin 150030, China |
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| Abstract: | Abstract: Many diagnosis expert systems for equine had been developed in the past, but there are no in-depth studies for the equine disease diagnosis expert system. And also most of the traditional expert systems are used by the conventional inference approach. The domain expert confidence factor (CF) for each rule of diseases is kept unchanged with its original value in conventional inference approach. So most of the traditional expert systems have a static knowledge base with static inference, and the decision power of these systems remains same through the life cycle of the system. In fact, the progress of equine knowledge requires modification of the knowledge base and rule base in the system. In this paper, we suggested a new approach for providing the intelligence in the system for diagnosis of the equine diseases. This experiment was conducted to develop a remote auxiliary equine diseases diagnosis expert system. By collecting and analyzing the experiences of diagnosis and treatment from experts on equine disease, the numerical expression of the equine diseases diagnosis knowledge was developed. The knowledge of equine diseases was represented with the method called object-attribute-value triples act (referred as O-A-V act) that combined with the generative formula. As such, it was easy to extract knowledge rules and these rules were used for inference mechanism. Using the confidence factor, multi-valued logic was used to represent the rules of confidence level. In this paper, we suggested a new inference method which was based on use of a fuzzy rule promotion theory. This approach can enhance the intelligence of the disease diagnosis system. If a rule was repeatedly used in corrective diagnostic results, it was then promoted to a higher confidence factor by the rule promotion factor (PCF), and the PCF was the original confidence factor in the next diagnosis session. In short, the dynamic PCF which was generated in the past dialogue was used instead of static CF in the final decision making process. The dynamically promoted rules were derived from those diagnosis sessions, which resulted in successful decisions. This enabled more efficient decision making in the future sessions. With this approach, it was not only decreasing the number of interactive between the system and the users, but also leading to acceptable diagnostic results. Based on the research of knowledge representation and inference mechanism, an auxiliary diagnostic expert system of equine diseases based on Microsoft.Net and SQL Server 2008 was designed and developed. It provided online help to equine farmers and extension workers in China. For the inference engine of system, we used the fuzzy rule promotion methodology that matched facts against conditions and determined which rules were applicable. Also, it automatically revised the confidence factor of each rule. The system performance test was conducted by 804 disease test cases. The successful and unsuccessful diagnosis consultation sessions were noted each time. By thirteen iteration tests, it showed that diagnostic accuracy of the system was closed to 92.28%. It proved that the method was a new way to enhance the diagnostic intelligence. The system met basic requirement of users. Suggestion for future improvement is needed to modify the rule promotion theory by minimizing the errors in the estimation of confidence factor and then estimation of the promoted confidence factor of the rule. By constantly maintaining and updating the knowledge base, and enriching the knowledge base, the system can definitely have a wide application in the countrywide. |
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| Keywords: | diseases diagnosis knowledge representation fuzzy rules rule promotion equine |
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