FWP加工变形补偿预测的ATS-FNN建模及仿真

针对柔性工件轨迹(FWP)加工变形影响因素复杂、对变形补偿预测模型实时性要求较高的问题,提出了FWP加工变形补偿预测的自适应TS模糊神经网络(ATS-FNN)建模方法.该方法利用自适应模糊聚类方法从历史加工数据中获取T-S型模糊神经网络(TS-FNN)前件网络的模糊隶属度函数、规则适应度;后件神经网络采用最速下降法作为学习算法,以较快地获得网络连接权值参数.仿真表明,文中构建的ATS-FNN比标准TS型模糊神经网络的建模时间减少52.34%,x、y方向补偿预测值的均方误差分别减少了36.50%和33.34%.

ATS-FNN-Based Modeling and Simulation for Compensation Prediction of FWP Machining Deformation

In order to meet the real-time requirements of the prediction model for deformation compensation of flexible workpiece path(FWP) with complex factors,a ATS-FNN(Adaptive TS Fuzzy Neural Network)-based mode-ling method for the compensation prediction of FWP machining deformation is proposed.In this method,the adaptive fuzzy clustering method is employed to obtain the antecedent fuzzy membership functions and fuzzy rule fitness of TS-FNN from historical machining data,and the steepest descent method is used as the learning algorithm of the consequent network to quickly calculate the parameters of connection weights.Simulated results indicate that,as compared with the standard TS-FNN,the ATS-FNN reduces a modeling time of 52.34% and a mean square error of the predicted compensation respectively by 36.50% or 33.34% in x or y direction.