摘要
This paper studies the generalization capability of feedforward neural networks (FNN).The mechanism of FNNs for classification is investigated from the geometric and probabilistic viewpoints. It is pointed out that the outputs of the output layer in the FNNs for classification correspond to the estimates of posteriori probability of the input pattern samples with desired outputs 1 or 0. The theorem for the generalized kernel function in the radial basis function networks (RBFN) is given. For an 2-layer perceptron network (2-LPN). an idea of using extended samples to improve generalization capability is proposed. Finally. the experimental results of radar target classification are given to verify the generaliztion capability of the RBFNs.
研究了前馈神经网络模式分类器的推广能力．从几何和概率两个方面分析了前馈网络用于模式识别的分类机理，在前人证明的基于误差最小的有导师（1和0）前馈网络输出端输出为模式样本后验概率估计结论的基础上，给出了径向基函数网络推广的核函数定理；对于两层感知器网络，提出使用加性噪声的样本来增加网络的推广能力．最后使用实测的五种飞机目标一维距离（纵向）象实验数据进行了模拟实验．
