一种改进的反向传播/柯西机网络

T.-T. Lee, Jin-Tsong Jeng
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引用次数: 0

摘要

为了克服反向传播(Bp)算法的收敛速度慢、局部极小和麻痹问题,Wasserman(1990)提出了一种联合反向传播/柯西(Bp/Cauchy)机。本文引入一种交换条件来改进反向传播/柯西机网络。为了说明所提方法的有效性,包括xor和学习未知函数的例子。结果表明,改进后的Bp/Cauchy机器比原Bp/Cauchy机器学习更有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An improved back-propagation/Cauchy machine network
To overcome the shortcomings of the backpropagation (Bp) algorithm, namely, slow convergence, local minimum, and paralysis problems, a combined backpropagation/Cauchy (Bp/Cauchy) machine has been proposed by Wasserman (1990). In this paper, a switching condition is introduced to improve the backpropagation/Cauchy machine network. To illustrate the effectiveness of the proposed method, examples of xor and the learning of a unknown function are included. Results show that the improved Bp/Cauchy machine is more effective in learning than the original Bp/Cauchy machine.<>
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