相空间重构文献综述可视化分析

doi:10.16182/j.issn1004731x.joss.201712030

摘要/Abstract

摘要： 为探究相空间重构理论至今为止的发展历程及前景,特以CiteSpaceⅢ可视化引文分析软件为工具,通过文献计量学研究方法,对来自于Web of Science平台的与“phase space reconstruction”相关1 500篇文献按时间分布、空间分布、引文及作者分布的情况进行了汇总整理,并以图、表的形式展现。根据关键词和共现图及关键词时区图,重点考察了相空间重构理论的研究热点及趋势。研究表明,该理论的发文量逐年增加且势头强劲;在工程、技术领域的应用越发广泛;与多种算法相结合,可提高非线性系统的分析与预测精度。

关键词: 相空间重构, 可视化分析, 研究热点, 研究趋势

Abstract: The primary object of the paper is to reveal the development process and prospect of phase space reconstruction theory. The technique applied is referred as CiteSpace Ⅲ, a visualized analysis software for citation. The research method of literature metrology was used to classify the fifteen hundred papers from the Web of Science platform related with “phase space reconstruction” into different priorities according to time distribution, spatial distribution, citation and author distribution. All results were displayed in figures and tables. The hot spot and trend of phase space reconstruction were discussed based on the keywords co-occurrence graph and keywords time zone map. The study showed that the amount of the publications on the theory increased year by year harply, and it has been applied more widely in the field of engineering and technology. Combined with a variety of algorithms, the accuracy of the analysis and prediction of nonlinear systems can be improved.

Key words: phase space reconstruction, visualization analysis, hot spot, research trend

中图分类号:

F830.9

孟力, 毕叶平. 相空间重构文献综述可视化分析[J]. 系统仿真学报, 2017, 29(12): 3167-3175.

Meng Li, Bi Yeping. Visual Analysis of Literature Review of Phase Space Reconstruction[J]. Journal of System Simulation, 2017, 29(12): 3167-3175.

参考文献

[1] 陈敏, 刘文华. 基于混沌理论的高速公路交通流预测研究[J]. 科学技术与工程, 2009, 9(2):478-481.
Chen Min, Liu Wenhua.Forecast Research of Traffic Flow for Freeway Based on Chaos Theory[J]. Science Technology and Engineering, 2009, 9(2): 478-481.
[2] 张玉梅, 吴晓军, 白树林. 交通流量序列混沌特性分析及DFPSOVF预测模型[J]. 物理学报, 2013, 81(19): 120-128.
Zhang Yumei, Wu Xiaojun, Bai Shulin, Chaotic Characteristic Analysis for Traffic Flow Series and DFPSOVF Prediction Model[J]. Acta Physica Sinica, 2013, 81(19): 120-128.
[3] 王朝勇, 孙延风, 裴志利. 基于相空间重构理论的单变量金融时间序列波动预警[J]. 商业研究, 2015, 58(2): 70-75.
Wang Chaoyong, Sun Yanfeng, Pei Zhili.Application of Phase Space Reconstruction for Volatility Early Warning Using Univariate Financial Time Series[J]. Commercial Research, 2015, 58(2): 70-75.
[4] 黄艳林, 李友荣, 肖涵, 等. 基于相空间重构与独立分量分析的局部独立投影降噪算法[J]. 振动与冲击, 2011, 30(1): 33-36.
Huang Yanlin, Li Yourong, Xiao han, et al. Local Independent Projection De-noising Algorithm Based on Phase-space Reconstruction Technique and Independent Component Analysis[J]. Journal of Vibration and Shock, 2011, 30(1): 33-36.
[5] 陈南祥, 魏杰. 混沌时间序列法在地下水位预测中的应用[J]. 水利与建筑工程学报, 2011, 21(6): 1-4.
Chen Nanxiang, Wei Jie.Application of Chaos Time Series Method in Groundwater Table Prediction[J]. Journal of Water Resources and Architectural Engineering, 2011, 21(6): 1-4.
[6] 刘树勇, 朱石坚, 俞翔. 确定相空间重构嵌入维数的研究[J]. 哈尔滨工程大学学报, 2008, 29(4): 374-381.
Liu Shuyong, Zhu Shijian, Yu Xiang.Determinating the Embedding Dimension in Phase Space Reconstruction[J]. Journal of Harbin Engineering University, 2008, 29(4): 374-381.
[7] 胡瑜, 陈涛. 基于C-C算法的混沌吸引子的相空间重构技术[J]. 电子测量与仪器学报, 2012, 26(5): 425-430.
Hu Yu, Chen Tao.Phase-space Reconstruction Technology of Chaotic Attractor based on C-C Method[J]. Journal of Electronic Measurement and Instrumentation, 2012, 26(5): 425-430.
[8] Kugiumtzis D.State Space Reconstruction Parameters in the Analysis of Chaotic Time Series—the Role of the Time Window Length[J]. Physica D: Nonlinear Phenomena (S0167-2789), 1996, 95(1): 13-28.
[9] Kim H S, Eykholt R, Salas J D. Nonlinear Dynamics, Delay Times,Embedding Windows[J]. Physica D: Nonlinear Phenomena (S0167-2789), 1999, 127(1): 48-60.
[10] 向昌盛, 张林峰. 混沌时间序列预测模型参数同步优化[J]. 计算机工程与应用, 2011, 48(1): 4-7, 11.
Xiang Changsheng, Zhang Linfeng.Simultaneous Optimization of Chaotic Time Series Prediction Model Parameters[J]. Computer Engineering and Applications, 2011, 48(1): 4-7,11.
[11] 王新迎, 韩敏. 多元混沌时间序列的多核极端学习机建模预测[J]. 物理学报, 2015, 83(7): 137-143.
Wang Xinying, Han Min.Multivariate Chaotic Time Series Prediction Using Multiple Kernel Extreme Learning Machine[J]. Acta Physica Sinica, 2015, 83(7): 137-143.
[12] Meng Li, Dong Lijun. Forecasting the Exchange Rate Using the Improved SAPSO Neural Network[J]. Advanced Materials Research (S1662-8985), 2012, 468-471: 1714-1720.
[13] Meng Li.Forecasting the Exchange Rate Based on BP Neural Network in Combination with Simulated Annealing[J]. Advanced Materials Research (S1662-8985), 2013, 753(10): 2930-2934.
[14] Meng Li, Sun Yang.Research on Automated Forex Trading System Based on BP Neural Network[J], Advanced Materials Research (S1662-8985), 2013, 753(10): 3080-3084.
[15] 孟力, 高鑫. 金融时间序列预测研究趋势可视化分析[J]. 情报科学, 2014, 32((增1): 79-84.
Meng Li. Gao Xin.Visualization Analysis of the Research trend of Financial Time—series Forecasting[J]. Information Science December, 2014, 32(S1): 79-84.
[16] Packard N H, Crutchfield J P, Farmer J D, et al.Geometry from a Time Series[J]. Physical Review Letters(S0031-9007), 1980, 45(9): 712-716.
[17] Ruelle D, Takens F.On the Nature of Turbulence[J]. Communications in Mathematical Physics (S0010-3616), 1971, 20(3): 167-192.
[18] Takens F.Detecting Strange Attractors in Turbulence[M]. Springer Berlin Heidelberg (S0301-0449), 1981: 366-381.
[19] Grassherger P, Procaccia I.Measuring the Strangeness of Strange Attractors[J]. Physica D: Nonlinear Phenomena (S0167-2789), 1983, 9(1): 189-208.
[20] 董安, 潘宏侠. 柴油机振动信号的分形特征及故障诊断[J]. 噪声与振动控制, 2014, 34(2): 144-147.
Dong An, Pan Hongxia.Fractal Characteristics and Fault Diagnosis of Vibration Signal of Diesel Engines[J]. Noise and Vibration Control, 2014, 34(2): 144-147.
[21] 付彧. 股票市场混沌特征量的提取及其分析[J]. 中外企业家, 2015, 32(4): 99-101.
Fu Yu.Extraction and Analysis of Chaotic Characteristics of Stock Market[J]. Chinese & Foreign Entrepreneurs, 2015, 32(4): 99-101.
[22] 方仍存, 李超顺, 李静. 基于相空间重构和QPSO- SVM的超短期负荷预测[J]. 中国农村水利水电, 2014, 56(5): 142-145, 154.
Fang Rengcun, Li Chaoshun, Li Jing.Ultra-short Term Load Forecasting Based on Phase Space Reconstruction and QPSO-SVM[J]. China Rural Water and Hydropower, 2014, 56(5): 142-145,154.
[23] 刘超, 刘东. 人民币对美元汇率波动的混沌性研究——基于2005年汇改后与汇改前数据的比较[J]. 金融理论与实践, 2014, 36(3): 12-18.
Liu Chao, Liu Dong.Chaotic Research of the RMB Against the Dollar Exchange Rate Volatility—Based on the Comparison of Data Before and After the Exchange Rate Reform in 2005[J]. Financial Theory & Practice, 2014, 36(3): 12-18.
[24] 张文超, 谭思超, 高璞珍. 基于Lyapunov指数的摇摆条件下自然循环流动不稳定性混沌预测[J]. 物理学报, 2013, 81(6): 61-68.
Zhang Wenchao, Tan Sichao, Gao Puzhen.Chaotic Forecasting of Natural Circulation Flow Instabilities Under Rolling Motion Based on Lyapunov Exponents[J]. Acta Physica Sinica,2013, 81(6): 61-68.
[25] 田中大, 李树江, 王艳红, 等. 短期风速时间序列混沌特性分析及预测[J]. 物理学报, 2015, 83(3):246-257.
Tian Zhongda, Li Shujiang, Wang Yanhong, et al.Chaotic Characteristics Analysis and Prediction for Short-term Wind Speed Time Series[J]. Acta Physica Sinica, 2015, 83(3):246-257.
[26] 顾秋宇, 朱捷, 徐晶. 混沌时间序列在股票价格长期预测中的应用[J]. 哈尔滨商业大学学报(自然科学版), 2015, 36(1): 108-111, 124.
Gu Qiuyu, Zhu Jie, Xu Jing.Application of Chaotic Time Series Prediction of Long-term Stock Price[J]. Journal of Harbin University of Commerce(Natural Sciences Edition), 2015, 36(1): 108-111, 124.
[27] 高鑫. 基于RPNN-SA-PSO混沌时间序列预测模型的程序化交易研究[D]. 厦门: 厦门大学, 2015:46-57.
Gao Xin.Programming Trading Research Based on the RPNN-SA-PSO Chaotic Time Series Prediction Model[D]. Xia Men:Xia Men University, 2015:46-57.