改进小波网络算法研究及应用

Yin Jin-tian, Tang Jie, Liu Li
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引用次数: 0

摘要

传统的基于BP方法的学习算法可能收敛到局部极小值,收敛速度慢,并且在收敛点前后都有震荡。提出了一种基于BP和PID的小波网络学习算法。而PIDBP算法在加入动量项后可以显著降低局部最小值出现的概率,同时引入惯性项,可以在较大的学习参数中加速收敛和发散并减少振荡的可能性,避免了传统BP算法在收敛区域不敏感加速收敛。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improved Wavelet Networks Algorithm Research and its Application
The conventional learning algorithm based on BP method may converge to a local minimum, slowly converging speed and is shock before and after the Convergence point. A algorithm based on BP and PID techniques for wavelet network learning was proposed. And PIDBP algorithm can significantly reduce the probability of the emergence of local minimum after adding momentum term, At the same time introduction of the inertia term, Can be in the larger learning parameters to speed up the convergence and divergence and to reduce the possibility of oscillation, And avoid conventional BP algorithm in the convergence region Insensitivity to accelerate the convergence.
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