【摘要】 为了在不依赖测试样本的前提下获取最优的径向基函数分布系数Opt-SPRD,从而构造出具有更高精度的径向基神经网络(RBNN)近似模型,提出了一种基于交叉验证的分布系数选取方法。该方法以分布系数与交叉验证误差之间的函数为基础,把对应于交叉验证误差最小值的分布系数作为Opt-SPRD的近似解。数值实验的结果表明,所提出的方法明显优于目前通行的缺省处理方法;与基于L-MBP算法的前馈神经网络近似模型相比,在所提出方法基础上构造出的RBNN近似模型具有更高、更稳定的精度。更多还原

【Abstract】 To obtain the optimum spread of radial basis functions (Opt_SPRD) without using test samples for constructing Radial Basis Neural Network (RBNN) approximation models with higher accuracy, a new method of choosing spread based on cross validation was proposed. This method took the function between spread and cross validation error as its basis, and took the spread corresponding with the minimum cross validation error as the approximation of Opt_SPRD. The results of numerical experiments indicate: the proposed method is superior to the current default method; compared with the feedforward neural network approximation models based on L-M backpropagation, the RBNN approximation models based on the proposed method produce smaller errors and have more steady performance.更多还原