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首页> 外文期刊>Heat transfer >Genetic algorithm-assisted artificial neural network for retrieval of a parameter in a third grade fluid flow through two parallel and heated plates
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Genetic algorithm-assisted artificial neural network for retrieval of a parameter in a third grade fluid flow through two parallel and heated plates

机译：遗传算法辅助人工神经网络，用于通过两个平行和加热板的三级流体中的参数检索参数

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Genetic algorithm (GA) has been used to determine important attributes of artificial neural network (ANN), such as number of neurons in different hidden layers and division of data for training, validation, and testing. The GA-assisted ANN (GAAANN) model was used to retrieve third grade fluid (TGF) parameter (A) in a TGF flow problem. The TGF was allowed to flow through two parallel plates, which were subjected to uniform heat flux. The least square method (LSM) was used to solve the governing equations, for specified boundary conditions. In this way, temperature profiles for different values of A were computed by LSM, constituting the direct part of the problem. In the inverse part, the GAAANN model was fed with a temperature profile as input and the corresponding value of A was obtained as output. Four different GAAANN model were developed, and a detailed analysis was done in retrieving the value of A by different GAAANN models. Two very important and commonly used algorithms: Levenberg-Marquardt (LM) and scaled conjugate gradient are explored for training of the neurons. The entire four GAAANN model were able to retrieve the value of A with different levels of accuracy.
机译：遗传算法（GA）已被用于确定人工神经网络（ANN）的重要属性，例如不同隐藏层中的神经元数量和用于训练，验证和测试的数据分划线。 GA辅助ANN（GaAann）模型用于在TGF流动问题中检索三级流体（TGF）参数（A）。允许TGF流过两个平行的板，其经受均匀的热通量。用于求解指定边界条件的控制方程，使用最小二乘法（LSM）。以这种方式，LSM计算不同值的温度分布，构成问题的直接部分。在逆部件中，通过输入的温度曲线馈送Gaaann模型，并获得作为输出的相应值。开发了四种不同的GaAann模型，并在检索不同的GaAann模型的值时进行详细分析。两个非常重要且常用的算法：Levenberg-Marquardt（LM）和缩放的共轭梯度被探索用于训练神经元。整个四个GaAann模型能够以不同的准确度检索A的值。

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