Using Case-based Reasoning To Establish An Expert System For Diagnosis Of Fish Disease

In this paper, Case-Based Reasoning is put forward to study fish disease diagnosis in order to solve the problem of knowledge acquisition bottleneck existing in the previous fish disease diagnosis expert systems. The advantage of CBR is following: 1) it just needs adequate cases and the regularization and pretreatment of expert knowledge is not necessary. 2) CBR is an efficacious method to simulate expert thinking with imagery.The paper discusses the presentation of a new case, the retrieval of the most similar cases from the case base, and the storage of all cases. Above all, we analyze the diagnosis process offish disease the fish expert have, and put forward a conception model of fish disease diagnosis according to the relation of case elements. Furthermore, we design the case representational formalism and the structure of case base according to the relation database theory.Fish cases are qualitative and discrete data, in view of these characteristics, binary system is used to represent the cases when we discuss the case retrieval strategy. On the basis of the above, two case retrieval methods are provided, one is similarity-testing model, and the other is rough analysis. The similarity-testing model realizes a mechanism of variant weights of the cases according to Tversky's contrast matching model. In the second method, rough analysis is introduced, which can fully use the reduction attributes of case database and make multi-index of case. The two case retrieval methods resolve the uncertainty offish disease from different points of view.Finally, according to the demand of expert system, the model of JSP added JavaBeans has been developed for implementing the fish disease diagnosis expert system under Windows 2003 Server. A three layers structure B/S/S used in WEB is adopted in the new fish disease diagnosis expert system.