基于径向基函数神经网络的电火花线切割机床可靠性数据模拟生成

Simulating and Extending Wire Electrical Discharge Machining Reliability Data by Radial Basis Function Neural Network

针对电火花线切割机床(Wire electrical discharge machining,WEDM)可靠性数据分布模型无法确定的问题,提出应用径向基函数(Radial basis function,RBF)神经网络对可靠性数据进行模拟仿真,扩大可靠性数据样本量,从而确定其分布模型的方法。选取聚类学习算法作为神经网络学习方法,通过无监督学习确定RBF神经网络中各隐节点的数据中心,并根据各数据中心之间的距离确定隐节点的扩展常数,然后用有监督学习训练各隐节点的输出权值。经过对原始可靠性数据进行拟合训练后生成一套RBF神经网络,随机产生100个可靠度数据输入该神经网络产生与原始可靠性数据具有相同失效统计规律的数据。对扩充后的可靠性数据通过图估计法和柯尔莫哥洛夫-斯米尔诺夫(Kolmogorov-smirnov,K-S)检验法确定电火花线切割机床可靠性数据分布模型为对数正态分布模型,同时对可靠性模型的参量估计更加准确。

For determining the distribution model of wire electrical discharge machining （WEDM） reliability data, the radial basis function （RBF） neural network is applied to simulating the original ：reliability data, and more reliability data are achieved that have the same distribution rules with the original reliability data. The cluster learning algorithm is chosen as the learning method of the neural networks. The data centers of hidden nodes are determined by unsupervised learning, and the extended constants of the hidden nodes are determined by the distances of each data center, then the output weights of the hidden nodes are achieved by the sups＇vised learning method. After simulating and calculating, the extended reliability data is achieved by the trained RBF neural networks, and the reliability distribution model of WEDM reliability data is confirmed as log-normal distribution model by the graphical estimation method and Kolmogorov-smiruov （K-S） test method. And it is more accurate for estimating characteristic parameters of the reliability distribution model.