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首页> 外文期刊>Natural Hazards and Earth System Sciences Discussions >An attempt to model the relationship between MMI attenuation and engineering ground-motion parameters using artificial neural networks and genetic algorithms
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An attempt to model the relationship between MMI attenuation and engineering ground-motion parameters using artificial neural networks and genetic algorithms

机译：试图利用人工神经网络和遗传算法模拟MMI衰减与工程地运动参数的关系

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摘要

Complex application domains involve difficult pattern classification problems. This paper introduces a model of MMI attenuation and its dependence on engineering ground motion parameters based on artificial neural networks (ANNs) and genetic algorithms (GAs). The ultimate goal of this investigation is to evaluate the target-region applicability of ground-motion attenuation relations developed for a host region based on training an ANN using the seismic patterns of the host region. This ANN learning is based on supervised learning using existing data from past earthquakes. The combination of these two learning procedures (that is, GA and ANN) allows us to introduce a new method for pattern recognition in the context of seismological applications. The performance of this new GA-ANN regression method has been evaluated using a Greek seismological database with satisfactory results.
机译：复杂的应用域涉及困难的模式分类问题。本文介绍了基于人工神经网络（ANNS）和遗传算法（气体）的MMI衰减和依赖于工程地面运动参数的模型。本调查的最终目标是评估基于宿主区域的地震模式的基于培训ANN的宿主地区为宿主区域开发的地区运动衰减关系的目标区域适用性。这个安学习基于使用过去地震的现有数据的监督学习。这两个学习程序的组合（即GA和ANN）允许我们在地震应用的背景下引入一种模式识别的新方法。通过具有令人满意的效果的希腊地震学数据评估了这种新的GA-ANN回归方法的性能。

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