RBF神经网络在冲压件毛坯优化设计中的应用

Application of RBF Neural Network on the Optimized Design of Blank Shape of Stampings

人工神经网络以其高度的并行处理信息的能力,能够有效地实现从输入到输出的非线性映射,广泛用于难以建立数学模型的非线性系统。本文将径向基函数(Radial Basis Function,RBF)神经网络引进到冲压件毛坯展开的优化设计当中,并结合MATLAB软件进行编程运算,通过实例证明了该方法的有效性。

Artificial neural network, for its strong power in parallel processing of information and it can effectively accomplish the nonlinear mapping from the input to the output, is widely used to the nonlinear systems that is difficult to establish the mathematical model. This article brings Radial Basis Function neural network into the optimized design of blank shape of stampings. With programming and computing in Matlab software, the example shows this method is effective.