改进BFO优化BPNN的自来水混凝加药预测

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Abstract In this paper, a prediction control model was proposed, which was designed with BPNN optimized by the hybrid algorithm with quantum particle swarm optimization (QPSO) and improved bacterial foraging (IBFO). In this strategy, the individual and population extremum of quantum particle swarmoptimizationwere used to update the bacterial positions in the chemotaxis process for BFO. The chemotaxis operator wasupgraded through bacteria synergy to improve the optimization accuracy. The reproduction operator was improved with difference method to solve the problem of partial dimension degradation. The roulette measure was applied as the selection mechanism to perfect the migration operator, which could overcome the disadvantage of the disappearance for the excellent solutions in the optimization process. Finally, the weights and thresholds of BP neural network were optimized to work out the coagulant dosage. Off-line training andtesting fordata model of one waterworks in Yunnan showed that the mean square error (MSE) of the prediction results of the proposed algorithm was 0.0116mg/L, and the mean absolute percentageerror (MAPE) was 1.36%, which weresuperior toBFO-BPNN and PSO-BPNN models in prediction accuracy and stability.

Key words： coagulation dosing prediction model BPNN BFO QPSO

Received: 22 February 2021

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TP273

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	Articles by authors
	ZHANG Chang-sheng
	HAN Tao
	QIAN Bin
	HU Rong
	TIAN Hai-yong
	MAO Hui
	WANG Zhuo

Cite this article:

ZHANG Chang-sheng,HAN Tao,QIAN Bin等. Prediction model for tap water coagulation dosing based on BPNNoptimizedwith improved BFO[J]. CHINA ENVIRONMENTAL SCIENCECE, 2021, 41(10): 4616-4623.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2021/V41/I10/4616

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