A Method for Predicting the Strength of High Performance Concrete Based on Memristor-Gradient Descent Neural Network
A high-performance concrete, gradient descent method, applied in neural learning methods, biological neural network models, instruments, etc., to achieve the effect of high strength prediction accuracy and low consumption
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[0086] The present invention excavates 104 groups from the literature (see literature C.H.Lim, Y.S.Yoon, J.H.Kim. Genetic algorithm in mixproportioning of high-performance concrete [J]. Cement and Concrete Research, 2004, 34 (3): 409-420.) Data, 92 groups are randomly selected as training samples, and the remaining 12 groups are used as prediction samples. The 104 groups of specific sample data values are shown in Table 1.
[0087] Table 1 sample data
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[0092] (1) In order to train the neural network established with the memristor, and considering the need to test the learned neural network, randomly select 12 groups (2, 10, 17, 30, 40, 48, 55, 63 , 75, 83, 93, 101 groups) as test data, and the remaining 92 data as training samples. For convenience, the screened test data are numbered 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, and 12 in sequence.
[0093] (2) Establish as figure 2 The memristor-gradient descent neural network show...
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