自适应神经模糊推理系统改进算法在机械加工参数优化中的应用

Application of Improved ANFIS in Optimization of Machining Parameters

在分析目前常用反向传播算法改进方法优缺点的基础上,提出用共轭梯度法对自适应模糊神经推理系统进行改进的训练算法,在训练中用Fletcher-Reeves方法计算上次搜索方向对新搜索方向的影响因数,在混沌时间序列预测和复杂非线性函数逼近的应用实例证明,改进后的算法收敛次数减少,训练速度加快。结合MATLAB的模糊工具箱,详述了如何在已有标准算法基础上进行算法改进。目前计算机辅助工艺设计受诸多复杂非线性问题的困扰发展缓慢,利用自适应模糊神经推理系统的自学习、自适应和逻辑推理能力,将改进后的算法用于逼近误差复映系数与工艺系统刚度、进给量等因素之间的非线性关系,实现机械加工参数的优化,提高工艺系统的自适应能力和工作效率,试验验证了此方法的可行性。

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.