Using a Hybrid Computational Approach Based on FCM for Classification of Diseases in Traditional Chinese Medicine Meridian Data

• Main Authors: Shih-Lin Lin, 林世麟
• Other Authors: Ta-Cheng Chen
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/6d3w5z

碩士 === 國立虎尾科技大學 === 資訊管理研究所 === 100 === A fuzzy cognitive map is the model for description of causation in an event. It has been applied to find the implicit relationship between variables. The more advanced in nature of questions, the best solution decision-makers will do. Previous literature shows that the construction of well defined FCM without experts’ attendance is impossible. In this study, we proposed a hybrid computation approach to construct the fuzzy cognitive maps. The proposed approach is composed of two evolutionary computation methods which genetic algorithm(GA) and particle swarm optimization(PSO). The purpose of GA is to decide the significant variables. Based on those variables selected by GA. PSO will construct the most appropriate FCM, i.e., the relationship matrix for the set of variables. The proposed computational approach is then applied to construct the most appropriate fuzzy cognitive map without any experts’ advice. In this study, the diagnosis of traditional Chinese medicines’ has been investigation of base on twelve-meridian data obtained by meridian energy analysis device (MEAD). The computational results show the proposed approach can provide higher classification accuracy than those of the approaches in literature or commercial software.