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                    基于改進差分進化和回聲狀態網絡的時間序列預測研究

                    許美玲 王依雯

                    許美玲, 王依雯. 基于改進差分進化和回聲狀態網絡的時間序列預測研究. 自動化學報, 2019, 45(x): 1?9 doi: 10.16383/j.aas.c180549
                    引用本文: 許美玲, 王依雯. 基于改進差分進化和回聲狀態網絡的時間序列預測研究. 自動化學報, 2019, 45(x): 1?9 doi: 10.16383/j.aas.c180549
                    Xu Mei-Ling, Wang Yi-Wen. Time series prediction based on improved differential evolution and echo state network. Acta Automatica Sinica, 2019, 45(x): 1?9 doi: 10.16383/j.aas.c180549
                    Citation: Xu Mei-Ling, Wang Yi-Wen. Time series prediction based on improved differential evolution and echo state network. Acta Automatica Sinica, 2019, 45(x): 1?9 doi: 10.16383/j.aas.c180549

                    基于改進差分進化和回聲狀態網絡的時間序列預測研究

                    doi: 10.16383/j.aas.c180549
                    基金項目: 國家自然科學基金(61702077), 中央高?;究蒲袠I務費(DUT16RC(3)123)資助
                    詳細信息
                      作者簡介:

                      許美玲:東北大學智能工業數據解析與優化教育部重點實驗室講師, 大連理工大學電子信息與電氣工程學部博士后. 主要研究方向為神經網絡和多元時間序列預測. E-mail: xuml@dlut.edu.cn

                      王依雯:大連理工大學電子信息與電氣工程學部碩士生. 主要研究方向為時間序列預測. E-mail: wangyiwen0802@163.com

                    Time Series Prediction Based on Improved Differential Evolution and Echo State Network

                    Funds: Supported by National Natural Science Foundation of China(61702077), Fundamental Research Funds for the Central Universities (DUT16RC(3)123)
                    • 摘要: 針對回聲狀態網絡無法根據不同的時間序列有效地選擇儲備池參數的問題, 本文提出一種新型預測模型, 利用改進的差分進化算法來優化回聲狀態網絡. 其中差分進化算法的縮放因子F、交叉概率CR和變異策略自適應調整, 以提高算法的尋優性能. 為驗證本文方法的有效性, 對Lorenz時間序列、大連月平均氣溫 ? 降雨量數據集進行仿真實驗. 由實驗結果可知, 本文提出的模型可以提高時間序列的預測精度, 且具有良好的泛化能力及實際應用價值.
                    • 圖  1  ESN結構示意圖

                      Fig.  1  Structure of ESN

                      圖  2  IDE-ESN算法流程圖

                      Fig.  2  Flow chart for IDE-ESN

                      圖  3  Lorenz-x(t)序列: IDE-ESN的預測曲線及誤差曲線

                      Fig.  3  Lorenz-x(t) series: prediction and error curves obtained by IDE-ESN

                      圖  4  Lorenz-x(t)序列: 不同模型的適應度曲線

                      Fig.  4  Lorenz-x(t) series: the curves of Fitness for different models

                      圖  5  大連月平均氣溫: IDE-ESN的預測曲線及誤差曲線

                      Fig.  5  Dalian monthly average temperature series: prediction and error curves obtained by IDE-ESN

                      圖  6  大連月平均氣溫: 不同模型的適應度曲線

                      Fig.  6  Dalian monthly average temperature series: the curves of Fitness for differential models

                      表  1  Lorenz-x(t)序列: IDE-ESN模型參數

                      Table  1  Lorenz-x(t) series: parameters in IDE-ESN

                      儲備池參數取值
                      儲備池規模50
                      稀疏度0.0210
                      譜半徑0.9589
                      輸入變化因子0.0600
                      下載: 導出CSV

                      表  2  Lorenz-x(t) 序列: 測試集仿真結果

                      Table  2  Lorenz-x(t) time series: prediction results on the test dataset

                      模型RMSENRMSESMAPE
                      AF-ESN2.0850e-061.8571e-072.7992e-07
                      PSO-ESN1.0139e-061.0211e-071.3613e-07
                      ELM1.8422e-036.6638e-042.1061e-04
                      TLBO-ESN7.7210e-071.6737e-071.0528e-07
                      IDE-ESN3.2156e-079.8008e-084.3089e-08
                      下載: 導出CSV

                      表  3  Lorenz-x(t) 序列: 不同模型的運行時間

                      Table  3  Lorenz-x(t) series: run time of different models

                      模型AF-ESNPSO-ESNTLBO-ESNIDE-ESN
                      時間1405.4289 s47.6972 s168.3124 s102.8856 s
                      下載: 導出CSV

                      表  4  大連月平均氣溫: IDE-ESN模型參數

                      Table  4  Dalian monthly average temperature-rainfall series: parameters in IDE-ESN

                      儲備池參數取值
                      儲備池規模47
                      稀疏度0.0206
                      譜半徑0.9802
                      輸入變化因子0.0459
                      下載: 導出CSV

                      表  5  大連月平均氣溫: 測試集仿真結果

                      Table  5  Dalian monthly average temperature series: prediction results for the test dataset

                      模型RMSENRMSESMAPE
                      AF-ESN1.80420.29020.1820
                      PSO-ESN1.65110.29560.1666
                      ELM5.42350.67040.5520
                      TLBO-ESN1.67260.20880.1708
                      IDE-ESN1.42150.27410.1440
                      下載: 導出CSV

                      表  6  大連月平均氣溫: 不同模型的運行時間

                      Table  6  Dalian monthly average temperature series: run time of different models

                      模型AF-ESNPSO-ESNTLBO-ESNIDE-ESN
                      時間347.1955 s10.5115 s31.1971 s15.1921 s
                      下載: 導出CSV
                      360彩票
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