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Artificial Neural Networks for the Prediction of Thermo Physical Properties of Diacetone Alcohol Mixtures

机译:人工神经网络预测双丙酮醇混合物的热物理性质

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A predictive method based on Artificial networks has been developed for the thermophysical properties of binary liquid mixtures of diacetone alcohol with benzene, chlorobenzene and bromobenzene at (303.15,313.15 and 323.15) K. In method 1, a committee ANN was trained using 5 physical properties combined with absolute temperature as its input to predict thermo physical properties of liquid mixtures. Using these data we found out the predicted data for intermediate mole fraction of different systems without conducting experiments. ANN with back-propagation algorithm is proposed, for Multi-pass Turning Operation and developed in MATLAB. Compared to other prediction techniques, the proposed ANN approach is highly accurate and error is
机译:已经开发了一种基于人工网络的预测方法,用于在(303.15,313.15和323.15)K下双丙酮醇与苯,氯苯和溴苯的二元液体混合物的热物理性质。在方法1中,使用5种物理性质训练了委员会ANN。结合绝对温度作为其输入来预测液体混合物的热物理性质。使用这些数据,我们无需进行实验即可发现不同系统的中间摩尔分数的预测数据。提出了一种具有反向传播算法的神经网络,用于多道次车削操作,并在MATLAB中进行了开发。与其他预测技术相比,所提出的ANN方法具有很高的准确性,并且误差为

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