一种改进的BP神经网络及其应用

Rui Mou, Qinyin Chen, Minying Huang
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引用次数: 2

摘要

BP神经网络的传统算法存在一些缺点,如在目标附近,如果学习因子太小,收敛速度可能太慢,如果学习因子太大,收敛修正过多,导致振荡甚至分散现象。同时,该算法的收敛速度非常慢,主程序容易陷入局部极小值。针对这些问题,本文对学习因子和Sigmoid函数进行了优化,并对传统的BP神经网络进行了改进。仿真分析结果的对比表明,改进算法的收敛性和准确性优于传统算法,并具有通过持续自学习提高评价结果准确性、应用中不受主观因素干扰等智能优势。
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 BP Neural Network and Its Application
The conventional algorithm of the BP neural network has some disadvantages such as in the vicinity of the target, if the learning factor is too small, the convergence may be too slow, and if the learning factor is too large, the convergence may be amended too much, leading to oscillations and even dispersing phenomenon. At the same time, the very slow speed of convergence and the main procedure is easily trapped into local minimum value. To tackle these problems, this paper optimizes the learning factor and the Sigmoid function, and improves the conventional BP neural network. The comparison of the results in the simulation analysis shows that the convergence and the accuracy of the improved algorithm are better than that of the conventional algorithm, and it has some intelligent advantages such as that the accuracy of the evaluation results can be improved by continuous self-learning, and there are not subjective factors interference in the application.
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