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NEURAL NETWORK MODELS FOR PREDICTING THE PROPERTIES OF CHEMICAL COMPOUNDS

机译:预测化学化合物性能的神经网络模型

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Neural networks are a universal tool used to investigate the dependences between the structure of organic compounds and a broad spectrum of their physicochemical properties.The potential of neural network modeling is not yet exhausted,as the increasing number of publications on their use indicates.Neural network models can solve both classification(for a discrete set of values of the modeled property)and regression problems(for continuous values of the modeled property).The reason for the popularity of neural network models in applied research is their clarity and the fact that no deep knowledge of mathematical statistics is required for their effective use.
机译:神经网络是一种通用工具,可用于研究有机化合物的结构与其广泛的理化性质之间的依赖性。神经网络建模的潜力尚未耗尽,因为有关其用途的出版物数量不断增加。模型可以同时解决分类(针对建模属性的离散值集)和回归问题(针对建模属性的连续值)的问题。神经网络模型在应用研究中盛行的原因是它们的清晰性和无为了有效地使用它们,必须具备深入的数学统计知识。

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