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首页> 外文期刊>Revista mexicana de fisica >Performance of artificial neural networks and genetical evolved artificial neural networks unfolding techniques
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Performance of artificial neural networks and genetical evolved artificial neural networks unfolding techniques

机译：人工神经网络的性能和遗传进化的人工神经网络展开技术

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With the Bonner spheres spectrometer neutron spectrum is obtained through an unfolding procedure. Monte Carlo methods, Regularization, Parametrization, Least-squares, and Maximum Entropy are some of the techniques utilized for unfolding. In the last decade methods based on Artificial Intelligence Technology have been used. Approaches based on Genetic Algorithms and Artificial Neural Networks have been developed in order to overcome the drawbacks of previous techniques. Nevertheless the advantages of Artificial Neural Networks still it has some drawbacks mainly in the design process of the network, vg the optimum selection of the architectural and learning ANN parameters. In recent years the use of hybrid technologies, combining Artificial Neural Networks and Genetic Algorithms, has been utilized to. In this work, several ANN topologies were trained and tested using Artificial Neural Networks and Genetically Evolved Artificial Neural Networks in the aim to unfold neutron spectra using the count rates of a Bonner sphere spectrometer. Here, a comparative study of both procedures has been carried out.
机译：使用邦纳球光谱仪，通过展开过程获得中子光谱。蒙特卡洛方法，正则化，参数化，最小二乘和最大熵是用于展开的一些技术。在过去的十年中，已经使用了基于人工智能技术的方法。为了克服现有技术的缺点，已经开发了基于遗传算法和人工神经网络的方法。尽管如此，人工神经网络的优点仍然存在一些缺点，主要是在网络的设计过程中，例如，架构的最佳选择和学习ANN参数。近年来，已经使用了结合人工神经网络和遗传算法的混合技术。在这项工作中，使用人工神经网络和遗传进化人工神经网络对几种人工神经网络拓扑进行了训练和测试，目的是使用邦纳球谱仪的计数率来展开中子谱。在此，已对两种方法进行了比较研究。

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