基于改进粒子群与神经网络的机械结合面法向刚度建模

Modeling of Machined Joints Normal Stiffness Using Modified PSO-BP Neural Network Algorithm

为了提高机械结合面法向接触刚度预测精度,提出一种改进粒子群优化算法,并用改进粒子群算法优化BP神经网络的参数组合,实现了粒子群和BP神经网络相结合的算法模型。将影响结合面法向接触刚度的因素进行了特征分析和定量化描述,并用该算法进行法向接触刚度预测和相对误差分析。计算结果表明,计算准确度可达92%,实现了多种影响因素组合下的机械结合面法向接触刚度的建模。

With the aim to improve forecasting accuracy of the normal contact stiffness of machined joints,the modified particle swarm optimizer（MPSO） algorithm was proposed.The BP neural network parameters were optimized by the MPSO algorithm.The normal contact stiffness of machined joints was forecasted under different experimental conditions,and the relative errors were analyzed.The results showed that the forecast precision could reach to 92%,and the contact stiffness of machined joints was modeled for various affecting factors.