利用PLS-RBF神经网络优化岩体强度参数

Sha Ma, Binglin Li
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引用次数: 0

摘要

中立网络在系统中的进一步发展在一定程度上受到了制约。3层RBF神经网络具有自学习和自记忆的能力,但有时由于变量之间存在严重的多重相关性,且观测值少而变量多,往往会导致学习瘫痪。偏最小二乘回归具有在具有强多重相关性的变量之间建立计算模型的优点,尤其对数据量少、变量多的情况特别有效。为此,提出了一种新的有效方法——改进的神经网络。基于偏最小二乘回归的神经网络。算例结果表明,改进后的方法计算量少,精度高,为岩体强度参数的确定提供了一种新的途径。其网络已得到广泛应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimezed Rock Mass Strength Parameter via PLS-RBF Neutral Network
The neutral network further development is restricted in the system to some extent. The 3 layers RBF neutral network has the ability that self-study and self-remember, but sometimes because of serious multi-correlation between the variables, and a few observations while many variables, there usually will result into paralyzing in study. The partial least square regression has its advantage of building the calculation model between the variables with strong multi-correlation, especially much effective on a few data and many variables. So a new and effective method-improved neutral network has been introduced. The neutral network based on the partial least square regression. The results of example show the improved method has a few calculations and high accuracy, and provide a new way for valuing the rock mass strength parameters. Its network has been applied extensively.
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