Prediction model for tap water coagulation dosing based on BPNNoptimizedwith improved BFO
ZHANG Chang-sheng1, HAN Tao1, QIAN Bin1, HU Rong1, TIAN Hai-yong2, MAO Hui3, WANG Zhuo1
1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China; 2. Yunnan Shuye Technology Co., Ltd, Kunming 650032, China; 3. Kunming Branch of North China Municipal Engineering Design and Research Institute Co., Ltd, Kunming 650051, China
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.
张长胜, 韩涛, 钱斌, 胡蓉, 田海湧, 毛辉, 王卓. 改进BFO优化BPNN的自来水混凝加药预测[J]. 中国环境科学, 2021, 41(10): 4616-4623.
ZHANG Chang-sheng, HAN Tao, QIAN Bin, HU Rong, TIAN Hai-yong, MAO Hui, WANG Zhuo. Prediction model for tap water coagulation dosing based on BPNNoptimizedwith improved BFO. CHINA ENVIRONMENTAL SCIENCECE, 2021, 41(10): 4616-4623.
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