本文關(guān)鍵詞：多隱層BP神經(jīng)網(wǎng)絡模型在徑流預測中的應用，由筆耕文化傳播整理發(fā)布。

參考文獻/References:

[1] Tokar A S, Johnson P A. Rainfall-runoff modeling using artificial neural networks. Journal of Hydrologic Engineering, 1999, 4:232–239.
[2] Sudheer K P, Gosain A K, Ramasastri K S. A data-driven algorithm for constructing artificial neural network rainfall–runoff models. Hydrological processes, 2000, 16 (6): 1325–1330.
[3] Senthil Kumar A R, Sudheer K P, Jain S K, et al. Rainfall–runoff modeling using artificial neural network: comparison of networks types. Hydrological processes, 2004, 19 (6): 1277–1291.
[4] Tayfur G, Singh V P. ANN and fuzzy logic models for simulating event-based rainfall-runoff. Journal of Hydrologic Engineering, 2006, 132:1321–1330.
[5] Antar M A, Elassiouti I, Alam M N. Rainfall–runoff modeling using artificial neural networks technique: a Blue Nile catchment case study. Hydrological processes, 2006, 20 (5): 1201–1216.
[6] Nourani, Vahid, ?zgür Kisi, et al. Two hybrid Artificial Intelligence approaches for modeling rainfall–runoff process. Journal of Hydrology, 2011,402 (1): 41-59.
[7] Kisi, Ozgur, Jalal Shiri, and Mustafa Tombul. Modeling rainfall-runoff process using soft computing techniques. Computers & Geosciences, 2013, 51: 108-117.
[8] Hsu K, Gupta H V, Sorooshian S. Artificial Neural Network Modeling of the Rainfall-Runoff Process[J]. Water resources research, 1995, 31(10): 2517-2530.
[9] 雷曉云，張麗霞，梁新平. 基于MATLAB工具箱的BP神經(jīng)網(wǎng)絡年徑流量預測模型研究-以塔城地區(qū)烏拉斯臺河為例. 水文, 2008, 28(1): 43-46.
[10] Sohail A, Watanabe K, Takeuchi S. Runoff analysis for a small watershed of Tono Area Japan by back propagation artificial neural network with seasonal data. Water resources management, 2008, 22(1): 1-22.
[11] 尚曉三，王棟. 兩種不同類型的水文模型在貴州典型巖溶地區(qū)的應用. 南京大學學報(自然科學), 2009, 45(3): 409-415.
[12] 崔東文. 多隱層BP神經(jīng)網(wǎng)絡模型在徑流預測中的應用.水文, 2013, 33(1): 68-73.
[13] Haykin S. Neural Networks:A Comprehensive Foundation.Newejexsey: Prentice-Hall inc,1999.
[14] 文明，張頂立，房倩等. 地鐵車站施工過程中地表沉降的NARXNN時間序列預測模型. 巖土力學與工程學報, 2015,34(增1):3306-3312.
[15] 潘麗莎，程曉卿，秦勇等. 基于NARX神經(jīng)網(wǎng)絡的輪重減載率預測.鐵道車輛, 2012, 50(9): 4-7.
[16] 吳啟蒙，魏明，龐雷等. 基于NARX神經(jīng)網(wǎng)絡的電子電路電磁脈沖響應建模. 高壓電器, 2013, 49(11): 62-68.
[17] Chai L, Qu Y, Zhang L, et al. Estimating time-series leaf area index based on recurrent nonlinear autoregressive neural networks with exogenous inputs. International journal of remote sensing, 2012, 33(18): 5712-5731.
[18] Aguilar-Lobo L M, Loo-Yau J R, Rayas-Sánchez J E, et al. Application of the NARX neural network as a digital predistortion technique for linearizing microwave power amplifiers[J]. Microwave and Optical Technology Letters, 2015, 57(9): 2137-2142.
[19] 趙良杰，夏日元，易連興等. 基于 NARX 模型的巖溶地下河日流量預測.水電能源科學, 2015, 33(5):19-25.
[20] 周毅，徐柏齡. 神經(jīng)網(wǎng)絡中的正交設計法研究.南京大學學報(自然科學), 2001, 37(1): 72-78.
[21] 黃鹍，陳森發(fā)，亓霞等. 基于正交試驗法的神經(jīng)網(wǎng)絡優(yōu)化設計. 系統(tǒng)工程理論方法應用, 2004, 13(3): 272-275.
[22] 苑玉鳳. 正交試驗結(jié)果的分析. 統(tǒng)計與決策, 2006, 209(5): 138-139.
[23] 中華人民共和國水利部. 水文情報預報規(guī)范. GB/T 22482-2008.

  本文關(guān)鍵詞：多隱層BP神經(jīng)網(wǎng)絡模型在徑流預測中的應用，由筆耕文化傳播整理發(fā)布。