Method for multi-disease diagnosis based on optimized symptom adjacent-searching clustering and SOM NN

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.