Application of Improved ANFIS in Optimization of

Machining Parameters

WU Xingxing^{1, 2}  ZHU Xilin²  YANG Huixiao²

(1. Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033;
2. College of Mechanical Science and Engineering, Jilin University, Changchun 130025)

Abstract: Training arithmetic of adaptive network-based fuzzy Inference system (ANFIS) is improved with conjugate gradient algorithm on the basis of analyses of common improving methods of back propagation algorithm. During training Fletcher-Reeves method is used to compute influence factor of last search direction to new search direction. It’s proved that it cost fewer iterations and time to converge with improved arithmetic than standard ANFIS arithmetic by applications in chaotic time-series prediction and approaching complex non-linear functions. How to improve arithmetic on the basis of standard arithmetic with fuzzy toolbox is enlarged on. At present development of computer aided process plan is slowed by many complex non-linear problems. In order to utilize the learning, adaptive and logic inference abilities of ANFIS to solve them, improved arithmetic is used to optimize machining parameters by approaching the non-linear relationship among error reflection coefficient and rigidity of machining system, feeding speed etc. In this way work efficiency and adaptability of machining system are improved. Feasibility of this method is validated by experiments.

Key words: Adaptive network-based fuzzy inference system(ANFIS)  Fuzzy logic  Back propagation  Error reflection  Parameters optimization  Machining

CLC No: TP273

吉林省科技发展基金资助项目(20040333). Received 20070117, received in revised form 20070731