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Calculation of Concrete Minarets Frequency by Neural Network

机译:用神经网络计算混凝土尖塔频率

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Determination of natural angular frequency of concrete minarets by artificial neural network with various supporting conditions is general goal of this research. For the subject of neural network , training or learning algorithms are applied .The most famous of network structure which is back propagation algorithm is applied in this study. This algorithm is a systematic method for training multi layer artificial neural network . Back propagation algorithm is based on gradient descant which means that it moves downward on the error declination and regulates the weights for the minimum error. In this research, the real frequency of concrete minarets is calculated first using SAP2000 program and is defined as a goal function for neural network , so that all outputs of the network can be compared to this function and the corresponding error can be calculated and so the best function will selected. Then, a set of inputs including dimensions or specifications of arches are made using MATLAB program. After the determination of algorithm and quantification of the network, the phases of training and testing of the results are carried out and the output of the network is created. It is concluded that the performance of the neural network is optimum and the errors are less than 8%, so, the network trains in different manner. Furthermore the time of frequency calculations in neural network is less than real analysis time that calculated by SAP2000 software and its precision is acceptable (less than 12%).
机译:本研究的总体目标是在各种支持条件下通过人工神经网络确定混凝土尖塔的自然角频率。对于神经网络,应用训练算法或学习算法。本研究中最著名的网络结构是反向传播算法。该算法是一种训练多层人工神经网络的系统方法。反向传播算法基于梯度反扫描,这意味着它会根据误差偏斜向下移动,并为最小误差调整权重。在这项研究中,首先使用SAP2000程序计算混凝土宣礼塔的实际频率,并将其定义为神经网络的目标函数,以便可以将网络的所有输出与该函数进行比较,并可以计算出相应的误差,因此将选择最佳功能。然后,使用MATLAB程序进行一组输入,包括拱的尺寸或规格。在确定算法并量化网络后,进行训练和测试结果的阶段,并创建网络的输出。结论表明,神经网络的性能最佳,误差小于8%,因此,神经网络的训练方式不同。此外,神经网络中频率计算的时间少于SAP2000软件计算的实际分析时间,其精度是可以接受的(小于12%)。

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