Adjusting Model of Base Idle Speed for Gasoline Engine Based on Adaptive Network-Based Fuzzy Inference System

• Main Authors: Chien-Tsung Yu, 尤建忠
• Other Authors: Wen-Hsien Ho
• Format: Others
• Language: zh-TW
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/34551209507224255471

碩士 === 國立高雄第一科技大學 === 機械與自動化工程所 === 94 === Abstract In this thesis, we adopt the experimental way to collect the revolution datas of the base idle speed for the gasoline engine. In our experiments, we use the intake valve clearance, the exhaust valve clearance and the ignition timing as the input variables and the engine revolution as the output variable. Through the training and examining of adaptive network-based fuzzy inference system (ANFIS), we can carry out the construction of predicting model to provide the available reference for maintenance operators'' adjustment the base idle speed of the gasoline engine. It is available not only to save the adjusting time and labor, but also reach the higher quality. In this thesis, for getting the better learning effect, we firstly propose the larger-is-better performance for the engine revolution as the quality feature. By using the Taguchi method, we can find out the significant factor for the said predicting model. Following that, we increase the levels of the mentioned-above significant factor to improve the learning iii effect of the predicting model. Then, we plan the training experiments and the examining experiments for the predicting model, respectively. Therefore, we obtain the training datas and the examining datas, respectively. The training datas are applied to construct the predicting model for the base idle speed of the engine revolution. In addition, the examining datas are employed to examine the accuracy for the said predicting model. At the same time, in the ANFIS, the types of membership function for the antecedent part will affect the accuracy of the predicting model. We will study the effect of membership function included 4 types (triangle function, trapezoid function, bell-shape function and Gauss function) on the accuracy of the predicting model, so that we can select the better membership function of the antecedent part for the predicting model. In the experimented example, we can find that the bell-shape membership function can get the least error and its output values are for the said predicting model much closer to the experimental values.