Improved Fast Tracking Echo State Network and PM2.5 Prediction
LIU Bin1,LI De-jian1,ZHAO Zhi-biao2,WU You2
1. Electrical Engineering College of Yanshan University, Qinghuangdao, Hebei 066004, China
2. Information Science and Engineering College of Yanshan University, Qinghuangdao, Hebei 066004, China
Abstract：Aiming at the problem that the recursive least squares echo state network has low prediction accuracy in noisy environments, an improved fast tracking echo state network was proposed. Firstly, the variable forgetting factor which can be adaptively adjusted was added into the cost function of network, and the output weights of the network were trained by the improved recursive least squares method to obtain a fast tracking echo state network. Then, the effectiveness of the proposed network was verified by classical Lorenz chaotic system. Finally, the grey correlation method was used to analyze the correlation between the relevant variables and PM2.5, and the auxiliary variable set of PM2.5 concentration value was established. The auxiliary variable set was input into the fast tracking echo state network to predict the PM2.5 concentration value. Experimental results show that compared with the traditional echo state network and the recursive least squares echo state network, the improved state echo network has better prediction accuracy and stronger anti-noise ability.
［1］Wu J L, Yin X L, Xiao H. Seeing permeability from images: fast prediction with convolutional neural networks［J］. Science Bulletin, 2018, 63(18): 1215-1222.
［2］刘浩然,崔静闯,卢泽丹,等. 一种改进的雁群扩展粒子群算法研究［J］. 计量学报,2019,40(3):498-504.
Liu H R,Cui J C,Lu Z D, et al. Study on an Improved Extended PSO Algorithm Based on Geese Flight. Acta Metrologica Sinica, 2019, 40(3): 498-504.
［3］White D R, Fischer J. The Boltzmann constant and the new kelvin［J］. Metrologia, 2015, 52(5): 213-216.
［4］王有贵, 吴双双, 陈红江. 称重传感器蠕变误差的神经网络补偿方法［J］. 计量学报, 2018, 39(4): 510-514.
Wang Y G, Wu S S, Chen H J. Neural network compensation method for creep error of weighing sensor［J］. Acta Metrologica Sinica, 2018, 39(4): 510-514.
［5］陆艺, 张培培, 王学影. 基于PSO-BP神经网络的关节臂式坐标测量机长度误差补偿［J］. 计量学报, 2017, 38(3): 271-275.
Lu Y, Zhang P P, Wang X Y. Length error compensation of articulated arm CMM based on PSO-BP neural network［J］. Acta Metrologica Sinica, 2017, 38(3): 271-275.
［6］Minkyu Choi, Jun Tani. Predictive Coding for Dynamic Visual Processing: Development of Functional Hierarchy in a Multiple Spatiotemporal Scales RNN Model［J］. Neural Computation, 2017, 30(1): 237-270.
［7］张一珂, 张鹏远, 颜永红. 基于对抗训练策略的语言模型数据增强技术［J］. 自动化学报, 2018, 44(5): 891-900.
Zhang Y K,Zhang P Y,Yan Y H. Language model data enhancement technology based on confrontation training strategy［J］.Automation journal,2018,44(5):891-900.
［8］Jaeger H, Haas H. Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication［J］. Science, 2004, 304(5667): 78-80.
［9］徐正阳, 路志英, 刘洪. 基于泄漏积分型回声状态网络的在线学习光伏功率预测［J］. 电力系统及其自动化学报, 2018, 30(2): 1-7.
Yang Z H, Lu Z Y, Liu H. Online learning photovoltaic power prediction based on leakage integral echo state network［J］. Journal of Power System and Automation, 2018, 30(2): 1-7.
［10］Antonelo E A, Camponogara E, Plucenio A. System Identification of a Vertical Riser Model with Echo State Networks［J］.IFAC PapersOnLine,2015,48(6):304-310.
［11］伦淑娴, 胡海峰. 基于罚函数内点法的泄露积分型回声状态网的参数优化［J］. 自动化学报, 2017, 43(7): 1160-1168.
Lun S X, Hu H F. Parameter Optimization of Leakage Integral Echo State Network Based on Penalty Function Interior Point Method［J］. Journal of Automation, 2017, 43(7): 1160-1168.
［12］Jaeger H. Survey: Reservoir computing approaches to recurrent neural network training［M］. Netherlands: Elsevier Science Publishers, 2009.
［13］韩敏, 许美玲, 王新迎. 多元时间序列的子空间回声状态网络预测模型［J］. 计算机学报, 2014, 37(11): 2268-2275.
Han M, Xu M L, Wang X Y. Subspace Echo State Network Prediction Model for Multivariate Time Series［J］. Journal of Computer Science, 2014, 37(11): 2268-2275.
［14］宋彤, 李菡. 基于小波回声状态网络的混沌时间序列预测［J］. 物理学报, 2012, 61(8): 90-96.
Song T, Li H. Prediction of chaotic time series based on wavelet echo state network［J］. Chinese Journal of Physics, 2012, 61(8): 90-96.
［15］杨理践, 邢燕好, 张佳. 基于电磁超声导波的铝板裂纹缺陷检测方法［J］. 仪器仪表学报, 2018, 39(4): 150-160.
Yang L J, Xing Y H, Zhang J. Crack detection method for aluminum plate based on electromagnetic ultrasonic guided wave［J］. Journal of Instruments and Instruments, 2018, 39(4): 150-160.
［16］Song X C. Beijing PM2.5 DATA［DB］. http://archive.ics.uci.edu/ml, 2018-01-19.
［17］Li R, Hardy R, Zhang W, et al. Chemical Characterization and Source Apportionment of PM2. 5 in a Nonattainment Rocky Mountain Valley［J］. Journal of Environmental Quality, 2018, 47(2): 238-238.
［18］Aldabe J, Elustondo D, Santamaria C, et al. Chemical characterisation and source apportionment of PM2.5 and PM10 at rural, urban and traffic sites in Navarra (North of Spain)［J］.Atmospheric Research,2011,102(1):191-205.
2020, Vol. 41 
