Application of Adaptive Neuro-Fuzzy Inference System to Construct and Analyze Defroster Model for Refrigerator

• Main Authors: Yung-Yao Chan, 詹詠堯
• Other Authors: Jyh-Horng Chou
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/13110113408815106334

碩士 === 國立高雄第一科技大學 === 系統資訊與控制研究所 === 98 === We all know the refrigerator which is very extensive application in commercially. At after starting all refrigerator run in twenty four hours. Refrigerator need much cost and energy during running, which the heater of defroster used energy too very large. Therefore, it is worth exploring the issue how to reasonably reduce defroster time to save-energy and completely defrost. In the thesis, we adopt the experimental way to investigate of the defroster model for refrigerator. In this experiment, we use the defroster frequency, the operating temperature and the defroster time as input factor and the defroster discharge water as the output factor. We firstly use the Taguchi method. Then, we can find out the significant factor for predicting model. Following that, we increase the levels of the significant factor to improve the learning effect of the predicting model. Then, we can plan the training experiments and the examining experiments for the predicting model, respectively. Therefore, we get the training data and the examining data, respectively. The training data are applied to construct the defroster model for refrigerator by the adaptive neuro-fuzzy inference system（ANFIS）. In the training to select different quantity of membership function and different types of membership function to find out the best combination for accuracy of prediction model. In addition, the examining data were employed to examine the accuracy for the predicting model. In the experimented, we find that the 3x5x3 quantity of membership function and triangle membership function which have the best of accuracy. Then, we can plot defroster discharge water distribution diagram which explore the efficiency of defroster time. We propose reasonable and save-energy of defroster time setting. In addition, we compare thirty six sets by way of experimental with predicting defroster discharge water. The results reveal that the defroster model can be used save-energy setting reference.