改进的快速跟踪回声状态网络及PM2.5预测

doi:10.3969/j.issn.1000-1158.2020.09.17

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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.

Key words： metrology PM2.5 prediction echo state network recursive least square method grey relational analysis

Received: 23 October 2018 Published: 28 August 2020

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	Articles by authors
	LIU Bin
	LI De-jian
	ZHAO Zhi-biao
	WU You

Cite this article:

LIU Bin,LI De-jian,ZHAO Zhi-biao, et al. Improved Fast Tracking Echo State Network and PM2.5 Prediction[J]. Acta Metrologica Sinica, 2020, 41(9): 1138-1145.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2020.09.17 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2020/V41/I9/1138

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