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NON-LINEAR DYNAMIC MODEL OF ROCK BURST BASED ON EVOLUTIONARY NEURAL NETWORK

机译:基于进化神经网络的岩爆非线性动力学模型

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摘要

The theory studies have showed that, rock burst is a kind of dynamic phenomenon of rock mass in mining, and is a kind of dynamic disaster from mining. The time series of magnitude is a very important exterior behavior of rock burst. The previous studies show that, to model this complicated non-linear time series, the neural network is a very good method. To overcome the shortcomings of traditional neural network, a new kind of evolutionary neural network based on immunized evolutionary programming proposed by author is proposed here. At last, the proposed evolutionary neural network model is verified by a real magnitude series of rock burst. And the result is compared with other method, such as grey system method. The results have showed that, evolutionary neural network model not only has high approaching precision, but also has high predicting precision, and is a good method to construct the non-linear model of rock burst. And this method can be used in a large number of engineering examples.
机译:理论研究表明,岩爆是采矿过程中岩体的一种动态现象,是采矿过程中的一种动态灾害。量级的时间序列是岩爆的非常重要的外部行为。先前的研究表明,对于此复杂的非线性时间序列建模,神经网络是一种非常好的方法。为了克服传统神经网络的不足,本文提出了一种基于免疫进化规划的新型进化神经网络。最后,通过岩爆的真实幅度序列验证了所提出的进化神经网络模型。并将结果与​​其他方法(例如灰色系统方法)进行比较。结果表明,进化神经网络模型不仅具有较高的逼近精度,而且具有较高的预测精度,是构造岩爆非线性模型的良好方法。而且该方法可以在大量工程实例中使用。

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