农业工程学报2011，Vol.27Issue(1)：215-222,8.DOI:10.3969/j.issn.1002-6819.2011.01.035
基于征兆邻搜索优化聚类和自组织映射神经网络的多病害诊断
Method for multi-disease diagnosis based on optimized symptom adjacent-searching clustering and SOM NN
摘要
Abstract
Complex processes have the characteristic of multifarious, and simultaneity multi-abnormality is familiar in the area. Aimed at this problem, the representations and descriptions of symptom with abnormality were analyzed. Based on an existing mono-fault (mono-disease) diagnosis method by Self-Organizing Map Neural Networks (SOM NN), a multi-fault (multi-disease) diagnosis model was developed. This proposed SOM NN-based model has three layers, it has no need to study multi-disease samples. According to the analysis, Euclidean distance was taken as the main discrimination, and the sufficiency and necessity of symptom adjacent-searching were analyzed. The adjacent-searching algorithm was optimized and improved. Taking tomato disease as an example, the disease symptoms were extracted, and the mapping relationship between disease and symptom were developed. Using the method, the correct cluster results of disease symptom combinations were obtained. This model can achieve an accurate diagnosis of multi-diseases. The simulation results show that the proposed model performs well and the proposed multi-disease diagnosis is effective.关键词
多病害诊断/人工神经网络/自组织映射/邻搜索优化/聚类分析/番茄病害Key words
multi-disease diagnosis/ artificial neural networks/ self-organizing map/ adjacent-searching optimization/cluster analysis/ tomato disease分类
信息技术与安全科学引用本文复制引用
张可,柴毅,匡金骏..基于征兆邻搜索优化聚类和自组织映射神经网络的多病害诊断[J].农业工程学报,2011,27(1):215-222,8.基金项目
中央高校基本科研业务费No.CDJRC10170005与No.CDJZR11170005 （）
