前馈神经网络的一种有效学习算法

本文提出了基于混合ＧＮ－ＢＦＧＳ法进行前馈神经网络学习的新算法，该算法结合ＧＮ法与ＢＦＧＳ法的特点，既利用了问题本身的特殊结构，又能取得超线性甚至二次渐近收敛率。与ＢＰ算法相比，这种算法可取得更快和更可靠的学习特性，在学习过程中利用该方法能够区分非零残量和零残量问题的特点，提出了自动调整隐单元数的方法，从而可以保证网络学习与归纳能力，示例系统的结果表明了所提方法的有效性。

An Efficient Learning Algorithm for Feedforward Neural Networks

A new learning algorithm for feedforward neural networks based on the hybrid GNBFGS method is presented. The algorithm combines the better features of both Gauss-Newton and BFGS methods,which make use of the special structure of the problem,and the order of convergence is superlinear even quadratic.It gives faster and more reliable learning property compared with the BP algorithm.By making use of the feature for distinguishing the non-zero-residual and zero-residual problem of hybrid GN-BFGS method,a technique to adjust the number of hidden units in learning process is proposed to ensure learning and generalization capabilities of networks.The results of sample systems show the effectiveness of the algorithm.