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首页> 外文期刊>Chemometrics and Intelligent Laboratory Systems >Memetic algorithms-based artificial multiplicative neural models selection for resolving multi-component mixtures based on dynamic responses
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Memetic algorithms-based artificial multiplicative neural models selection for resolving multi-component mixtures based on dynamic responses

机译：基于模因算法的人工乘性神经模型选择基于动态响应的多组分混合物解析

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摘要

The potential of the product unit neural networks built by using different memetic evolutionary algorithms for the simultaneous determination of mixtures of analytes based on dynamic responses was investigated. For this purpose, three methodologies for obtaining the structure and weights of neural networks were proposed, based on the combination of the evolutionary programming algorithm, a clustering process and a local improvement procedure carried out by the Levenberg-Marquardt algorithm. To test these approaches, two phenol derivatives, pyrogallol and gallic acid, were quantified in mixtures based on their perturbation effect in a classical oscillating chemical system, namely, the Belousov-Zhabotinskyi reaction. The four-parameter Weibull curve associated with the profile of perturbation response estimated by the Levenberg-Marquardt method was used as input data for the models. Straightforward network topologies 4:3:1 (13 weights) and 4:2:1 (9 weights) for pyrogallol and gallic acid, respectively, allowed the analytes to be quantified with great accuracy (mean standard error of prediction for the generalization test) and precision (standard deviation) of 2.45percent and 0.21 for pyrogallol and 7.61percent and 1.63 for gallic acid. The selected model can be easily implemented via software by using simple quantification equations, from which significant chemical remarks can be inferred. Finally, the product unit neural network modelling offered better results when compared with sigmoidal neural networks and response surface analysis.
机译：研究了使用不同的模因进化算法构建的产品单元神经网络，用于基于动态响应同时确定分析物混合物的潜力。为此，基于进化规划算法，Levenberg-Marquardt算法进行的聚类过程和局部改进程序的组合，提出了三种获取神经网络结构和权重的方法。为了测试这些方法，根据混合物在经典振荡化学系统（即Belousov-Zhabotinskyi反应）中的摄动效应，对混合物中的两种苯酚衍生物，邻苯三酚和没食子酸进行了定量。与Levenberg-Marquardt方法估计的扰动响应曲线相关的四参数Weibull曲线用作模型的输入数据。邻苯三酚和没食子酸的直截了当的网络拓扑结构4：3：1（13重量）和4：2：1（9重量）允许对分析物进行高精度的定量（泛化测试的平均预测标准误差）邻苯三酚的精密度（标准偏差）为2.45％和0.21，没食子酸的精密度（标准偏差）为7.61％和1.63。通过使用简单的定量方程，可以通过软件轻松地实现所选模型，从中可以推断出重要的化学标记。最后，与S型神经网络和响应面分析相比，产品单元神经网络建模提供了更好的结果。

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