Abstract
In the library, the prediction and estimation of book borrowing plays an important role in library work. Based on the data mining method, this paper analyzed the prediction and estimation of book borrowing. Firstly, the radial basis function neural network (RBFNN) was analyzed. Then, the improved ant colony algorithm (IACO) was used to obtain the optimal parameters of RBFNN, and then the IACO-RBFNN model was established to realize the prediction and estimation of book borrowing. The results showed that the improved model had advantages in training time, iteration times, and error compared with BPNN and RBFNN. The results of book prediction and estimation showed that the results obtained by the IACO-RBFNN model were closer to the actual book borrowing situation, with smaller error and higher precision (97.09%), and its precision was 11.18% and 4.74% higher than BPNN and RBFNN respectively. The training time and testing time of the IACO-RBFNN model were 5.12 s and 1.03 s, respectively, which were significantly shorter than the other two methods. The results show that the IACO-RBFNN model has a good performance in the prediction and estimation of book borrowing and can be further promoted and applied in practice.
Highlights
The library is an important facility in a school
During the training of the improved ant colony algorithm (IACO)-radial basis function neural network (RBFNN) model, the data from January 2018 to December 2019 was used as training samples, the number of books borrowed in one month as one sample
The data of the fourth month were predicted based on the data of the first three months, for example, estimating the data of April 2018 based on the data of January 2018 ~ March 2018, i.e., the number of books borrowed in January, February, and March 2018 were taken as the input of the RBFNN model, and the number of books borrowed April 2018 was taken as the Training time/s Number of iterations Error
Summary
The library is an important facility in a school. It can meet the needs of teachers and students in teaching and scientific research by collecting and sorting books. Zhang et al [5] combined the long short-term memory method with the recurrent neural network to predict the remaining life of lithium-ion batteries. Aiming at the problem of urban traffic flow prediction, Hu et al [9] established GSTARSVM model with wavelet transform and predicted the short-term traffic flow. Through experiments, they found that the model had high prediction accuracy. The application of methods such as data mining and machine learning in library management is seldom, and artificial method is highly dependent, which is not conducive to the scientific management of a large number of books. Based on RBFNN, this study applied RBFNN to the prediction and estimation of book borrowing and optimized it with
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