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結合粒子群算法的神經網絡預測模型

發表時間:2019-08-30? 瀏覽量:272? 下載量:41
全部作者: 張煌,梁朋,肖琨武,甘俊哲,施三支
作者單位: 長春理工大學理學院;長春理工大學光電工程學院;長春理工大學電子信息工程學院
摘 要: 股票數據量大,走勢不穩定,峰值尖銳且通常噪聲較多,這些因素都給預測帶來了一定的難度,而目前現有的單一方法預測模型往往準確率較低。故針對現有預測模型的現狀,本文意圖將多種算法相結合從而尋求出一種最優的組合模型。滬深300指數樣本的選擇覆蓋了大部分證券市場,具有很強的代表性,因此本文針對滬深300指數數據首先比較了同樣為前饋式神經網絡的徑向基函數 (radial basis function,RBF)與BP(back propagation )兩種神經網絡模型,通過兩種模型的比較得出在滬深300指數的預測上BP神經網絡更具有優越性。因此,基于BP神經網絡模型,本文加入了粒子群算法與之相結合,先針對滬深300指數數據進行預處理,尋求最優權值,再利用BP神經網絡進行學習分析,有效減少了模型的誤差,最終的準確率能夠達到99.13%. 因此,結合粒子群算法的BP神經網絡模型能夠明顯減小誤差,提高預測的精確度。
關 鍵 詞: 計算數學;滬深300指數預測;粒子群算法;徑向基函數神經網絡;BP神經網絡
Title: Neural network prediction model combined with particle swarm optimization
Author: ZHANG Huang, LIANG Peng, XIAO Kunwu, GAN Junzhe, SHI Sanzhi
Organization: School of Science, Changchun University of Science and Technology; School of Optoelectronic Engineering, Changchun University of Science and Technology; School of Electronic Information Engineering, Changchun University of Science and Technology
Abstract: Stock data is large, and its trend is unstable. The peak of it is sharp and it has a lot of noise. These factors all bring some difficulties to the prediction. However, the existing single method prediction model tends to have lower accuracy. Therefore, in view of the current status of existing prediction models, this paper intends to combine various algorithms to find an optimal combination model. The selection of the CSI 300 index samples covers a strong representation of most securities markets. Therefore, this paper first compares the radial basis function (RBF) and back propagation (BP) of the feedforward neural network for the CSI 300 index data. Compared with two neural network models, the comparison results obtained by the two models show that the BP neural network is superior in the prediction of the CSI 300 index. Therefore, based on the BP neural network model, this paper combines the particle swarm optimization algorithm and combines it with the pre-processing of the CSI 300 index data to find the optimal weight. Then BP neural network is used for learning analysis, which effectively reduces the error of the model. The final accuracy rate can reach 99.13%. Therefore, the BP neural network model combined with the particle swarm optimization algorithm can significantly reduce the error and improve the accuracy of the prediction.
Key words: computational mathematics; CSI 300 index prediction; particle swarm optimization; radial basis function neural network; BP neural network
發表期數: 2019年8月第4期
引用格式: 張煌,梁朋,肖琨武,等. 結合粒子群算法的神經網絡預測模型[J]. 中國科技論文在線精品論文,2019,12(4):537-544.
 
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