图书馆数字资源聚合质量预测模型构建——基于改进遗传算法和BP神经网络  

Construction of Aggregation Quality Predicting Model for Digital Resource in Library——Based on Improved Genetic Algorithm and BP Neural Network

作　　者：闫晶 毕强[1] 李洁[1] 王福[1] 

机构地区：[1]吉林大学管理学院,长春130022 [2]东北电力大学经济管理学院,吉林132012

出　　处：《数据分析与知识发现》2017年第12期49-62,共14页Data Analysis and Knowledge Discovery

基　　金：国家自然科学基金项目"语义网络环境下数字资源多维度聚合与可视化研究"(项目编号:71273111)的研究成果之一

摘　　要：【目的】针对图书馆数字资源聚合质量评价要求,基于遗传算法对BP神经网络进行改进,进而构建更为优化的图书馆数字资源聚合质量预测模型。【方法】利用遗传算法计算简单、对待求解问题依赖小、并发线程计算速度快等优点,通过广义海明距离定义种群提高种群多样性,进行种群选择、交叉、变异操作,求解初始权重和阈值;将改进的遗传算法引入BP神经网络,通过权重和阈值的不断调整,快速收敛至适应度设定值,最终实现预测结果的进一步优化。【结果】采用MATLAB R2014a平台进行仿真实验,预测结果平均误差2.74E-04,同实际数据误差小,模型精度较高。程序运行总时长18.56秒,且三步就收敛到误差目标,模型收敛速度快,相较单一的遗传算法和BP算法具有更高的预测精度和效率。【局限】样本数据质量有待提高;实验中未采用Train的其他快速训练函数进行训练时间和预测精度对比;种群数量因计算复杂性而受限。【结论】模型能够对图书馆数字资源聚合质量做出高效、客观预测,应用前景和延展性较好,能有效运用于图书馆数字资源聚合质量评价结果检验、大样本评价以及大样本预测领域。[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 R2014 a platform to examine the proposed model and the average number of prediction errors was 2.74 E-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.

关 键 词：数字资源 聚合质量 模型构建 遗传算法 BP神经网络 

分 类 号：G250.73[文化科学—图书馆学] TP18[自动化与计算机技术—控制理论与控制工程]