神經(jīng)網(wǎng)絡(luò)在高校圖書館圖書借閱流量預(yù)測中的應(yīng)用
發(fā)布時間：2019-06-10 10:29
【摘要】：由于高校圖書館圖書借閱流量具有一定的非線性特性,傳統(tǒng)的回歸分析、灰色模型等方法難以處理這種非線性時間序列問題,影響了預(yù)測精度。為了提高預(yù)測精確度,提出粒子群優(yōu)化RBF神經(jīng)網(wǎng)絡(luò)的圖書借閱流量預(yù)測模型。該方法以圖書館圖書借閱流量歷史數(shù)據(jù)進行RBF神經(jīng)網(wǎng)絡(luò)建模,采用粒子群算法對RBF神經(jīng)網(wǎng)絡(luò)參數(shù)進行優(yōu)化,最后建立了圖書借閱流量動態(tài)響應(yīng)模型。預(yù)測結(jié)果表明該模型預(yù)測結(jié)果合理,精度較高,為圖書館提高工作效率和服務(wù)質(zhì)量提供了參考依據(jù)。
[Abstract]:Because the book lending flow of university library has certain nonlinear characteristics, the traditional regression analysis, grey model and other methods are difficult to deal with this nonlinear time series problem, which affects the prediction accuracy. In order to improve the prediction accuracy, a book loan flow prediction model based on particle swarm optimization RBF neural network is proposed. In this method, the historical data of library book lending flow are used to model the RBF neural network, and the particle swarm optimization algorithm is used to optimize the parameters of RBF neural network. Finally, the dynamic response model of book lending flow is established. The prediction results show that the prediction results of the model are reasonable and the accuracy is high, which provides a reference for the library to improve the work efficiency and service quality.
【作者單位】： 廣西師范學(xué)院圖書館;
【分類號】：G250.7;G258.6;TP183
[Abstract]:Because the book lending flow of university library has certain nonlinear characteristics, the traditional regression analysis, grey model and other methods are difficult to deal with this nonlinear time series problem, which affects the prediction accuracy. In order to improve the prediction accuracy, a book loan flow prediction model based on particle swarm optimization RBF neural network is proposed. In this method, the historical data of library book lending flow are used to model the RBF neural network, and the particle swarm optimization algorithm is used to optimize the parameters of RBF neural network. Finally, the dynamic response model of book lending flow is established. The prediction results show that the prediction results of the model are reasonable and the accuracy is high, which provides a reference for the library to improve the work efficiency and service quality.
【作者單位】： 廣西師范學(xué)院圖書館;
【分類號】：G250.7;G258.6;TP183
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