Construction of Aggregation Quality Predicting Model for Digital Resource in Library ——Based on Improved Genetic Algorithm and BP Neural Network
Yan Jing1,2(), Bi Qiang1, Li Jie1, Wang Fu1
1School of Management, Jilin University, Changchun 130022, China 2School of Economic Management, Northeast Electric Power University, Jilin 132012, China
[Objective] This paper proposes a model to predict the quality of library digital resource aggregation with the help of improved BP neural network based on genetic algorithm. [Methods] The genetic algorithm is simple in computing, less dependent on the problems to be solved, and could quickly calculate concurrent threads. First, we obtained the initial weight and threshold with increased population diversity,selection, crossover and variation. Second, we introduced the improved genetic algorithm to the BP neural network, which rapidly reached the fitness setting level by constantly adjusting the weight and threshold values. Finally, we further optimized the performance of the prediction model. [Results] We used MATLAB R2014a platform to examine the proposed model and the average number of prediction errors was 2.74E-04, which was smaller than the actual data. It took the program 18.56 seconds or three steps to finish the task. The prediction accuracy and efficiency of the proposed model was better than the single genetic or BP algorithms. [Limitations] The quality of sample data needs to be improved. We did not compare our training time and prediction accuracy with those of other quick training functions. The population numbers are limited due to computational complexity. [Conclusions] The proposed model could predict the quality of digital resource aggregation efficiently and objectively.
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