多几何要素影响下液压阀件特性的混合神经网络预测模型

液压阀件系统是一个具有多几何要素影响的多系统特性复杂系统,建立液压阀件特性预测模型,以系统多几何要素作为输入,实现系统特性的预测,将对实际生产具有重要的意义。在深入分析反向传播(Back propagation,BP)神经网络与径向基函数(Radial basis function,RBF)神经网络的基础上,结合两类神经网络的特点,提出基于BP神经网络与RBF神经网络的混合神经网络预测模型。利用生产实际中实测的某具体液压阀件特性值及影响该特性的各几何要素值作为预测模型的数据来源,对所提出的混合神经网络进行训练,并进行仿真。实例计算表明混合神经网络预测模型可提高单项神经网络预测模型的预测精度,预测平均相对误差为0.0461。可见,所提出的混合神经网络预测模型能够很好地满足工程实践中液压阀件特性预测要求。

Hybrid Neural Network Prediction Model of Hydraulic Valve Characteristics Under the Affection of Multiple Geometric Factors Effected

Hydraulic valve system is a complex system with multiple characteristics affected by multiple geometric elements. It will be essentially important to the manufacture process to establish the prediction model of the system characteristics by using the geometric elements and achieve the goal of prediction. On the basis of synthesizing the features of the back propagation (BP) neural network and RBF neural network,a prediction model which is a new hybrid neural network based on the BP neural network and radial...