用RBF神经网络预测材料力学性能的研究

An application of RBF neural network to mechanical properties of wheat straw -reinforced composite prediction

由于径向基函数(RBF)神经网络有易学,动态仿真性强,较强的输入输出映射功能和全局最优逼近的结构特点,因此将之用于预测麦杆增强复合板材力学性能。高斯函数表示形式简单,径向对称,光滑性好和解析性好,所以模型采用高斯函数作为隐含层基函数,k均值聚类法确定径向基函数的参数,运用最小二乘法确定权值。结合影响复合板材力学性能因素的特点和变化规律,以成型温度、成型压力、纤维含量、保温时间、拉伸强度、冲击韧性等为对象建立预测复合板材力学性能的模型,用它来优化模压成型的工艺参数,找出最佳工艺参数的范围。结果表明,径向基函数神经网络具有较好的学习和泛化能力,在预测力学性能中效果较好。

Constitutive features of radial basis function （RBF） ,such as fast neural network learning,strong dynamic simulation ability, good input/output mapping function, and global optimal approaching,were introduced. RBF Neural Network is used in Mechanical Properties of wheat straw-reinforced composite prediction. The algorithm for selecting the radial basic function center is the nearest neighbor algorithm. The molding and forecasting about temperature and molding pressure show that the network has rein-forcement learning capacities and mapping abilithy. Ideal result are obtained in Mechanical Properties of composite prediction.