Study And Establishment Of An Expert System For Diagnosis Of Chicken's Disease Based On Neural Network

In this paper the complex relationship between the twenty-one kinds of clinic common chicken's diseases and their symptoms, lesions was studied with the neural network and the case-based reasoning (CBR) technology, and an expert system for diagnosis of chicken's disease was finally built based on the neural network array principle and the standard cases on the basis of the CBR and their matching algorithm. When this system was tested in a simulated diagnosis with direct neural network diagnosis, there would be 53.2% and 12.8% at the rate of exactness and failure respectively, and 34% could be defined within 2 or 3 kinds of possible diseases. Testing of this system by CBR diagnosis, the actual cases, whose matching degrees lay in the first or the second place, could make up 57.5%. When the multiform diagnosis of "direct diagnosis", "sorting diagnosis", "identifying diagnosis" and "CBR diagnosis" were used in the system, the rate of exactness could be raised to 70.2%, failure reduced to 12.8% and other to 17% defined within 2 or 3 kinds of possible diseases. So the system could generally fulfill the requirement for the application in veterinary clinic diagnosis. It was found that the rate of exactness in diagnosis could be further raised if the disease cases were added into the system by taking IBD (infectious bursal disease) as an example.Because more computer technologies (multimedia, hypertext et al) were used, this system would be provided with a friendlier man-computer interface, a stronger capability and easier operation; its knowledgebase and case base could also be modified and enlarged. A browsing system of the knowledge of the chicken breeding and the chicken disease prevention was built with hypertext technology. In addition, a VCD of breeding laying chicken was also built.Result in this study showed that it was feasible completely to constructing veterinary expert system with neural network and CBR technology. The keys to building of a better system might be learning samples with high quality, fitting learning algorithm and reasoning method.