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Prediction of Viscosities of Aqueous Two Phase Systems Containing Protein by Artificial Neural Network

机译:人工神经网络预测含蛋白质的水两相体系的粘度

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The viscosities of aqueous two phase system containing bovine serum albumin (BSA) were predicted by artificial neural network (ANN) as a function of concentration of poly-ethylene-glycol (PEG), concentration of BSA and temperature. A three layer feed forward neural network based on Levenberg-Marquardt (LM) algorithm which consisted of three input neurons, 10 hidden neurons and one output neuron (3:10:1) was developed. The performance parameters were calculated and compared with the conventional Grunberg-Nissan empirical model. The satisfactory values suggest that the proposed ANN model has the capability of predicting viscosity in a better way than the conventional empirical model.
机译:通过人工神经网络(ANN)预测包含牛血清白蛋白(BSA)的水两相系统的粘度与聚乙二醇(PEG)浓度,BSA浓度和温度的关系。建立了基于Levenberg-Marquardt(LM)算法的三层前馈神经网络,该网络由三个输入神经元,10个隐藏神经元和一个输出神经元(3:10:1)组成。计算性能参数,并将其与常规的Grunberg-Nissan经验模型进行比较。令人满意的值表明,与传统的经验模型相比,所提出的ANN模型具有更好的预测粘度的能力。

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