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Optimezed Rock Mass Strength Parameter via PLS-RBF Neutral Network

机译:PLS-RBF神经网络优化的岩体质量参数

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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.
机译:中性网络进一步发展在某种程度上限制了系统。 3层RBF中性网络具有自学和自我记忆的能力,但有时由于变量之间的严重多相关,以及许多变量的几个观察,通常会导致研究瘫痪。 部分最小二乘回归具有其优点,其利用具有强大的多相关的变量之间的计算模型,特别是在几个数据和许多变量上有效。 因此,介绍了一种新的和有效的方法改进的中性网络。 基于部分最小二乘回归的中立网络。 结果示例显示了改进的方法具有少量计算和高精度,并为重视岩体质量参数提供了一种新的方式。 它的网络已被广泛应用。

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