Abstract The efficiency of artificial neural networks (ANNs) for modeling the electrical impedance spectroscopy of (4E)-2-amino-3-cyanobenzo[b]oxocin-6-one was investigated. The experimental data for electrical impedance and dissipation factor were used as input data for the model. The optimum network structure was obtained by testing different numbers of neurons with altered transfer functions to normalize the data. This structure simulated the experimental data with a very high accuracy and predicted new values that were untested experimentally. A nonlinear equation indicates the relation between inputs and output was introduced based on ANN model. The performances of the optimum network are obtained. Finally, this study showed that neural networks are a very effective tool in modeling and are able to follow the patterns of the experimental data with a high precision.

中文翻译：

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通过人工神经网络对 (4E)−2-amino-3-cyanobenzo[b]oxocin-6-one 的电阻抗谱建模

摘要 研究了人工神经网络 (ANN) 对 (4E)-2-amino-3-cyanobenzo[b]oxocin-6-one 的电阻抗谱建模的效率。电阻抗和耗散因子的实验数据用作模型的输入数据。通过测试具有改变传递函数的不同数量的神经元以对数据进行归一化，获得了最佳网络结构。这种结构以非常高的精度模拟了实验数据，并预测了未经实验测试的新值。基于人工神经网络模型引入了一个非线性方程来表示输入和输出之间的关系。获得最优网络的性能。最后，