基于量子迭代混沌的涡流搜索算法预测锅炉飞灰含碳量
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摘要
为了准确建立锅炉飞灰含碳量预测模型,首先提出了基于量子比特的Bloch球面坐标编码和迭代混沌映射的改进涡流搜索(I-VS)算法,然后对I-VS算法、涡流搜索(VS)算法、粒子群优化(PSO)算法、正余弦(SCA)算法和樽海鞘群(SSA)算法的性能进行比较。基于某热电厂300 MW循环流化床锅炉现场运行数据,采用I-VS算法优化并行感知机的极端学习机(PELM),得到飞灰含碳量的综合预测模型(即I-VS-PELM模型)。最后将I-VS-PELM模型的预测结果与PELM、PSO-PELM、SCA-PELM、SSA-PELM和VS-PELM模型的预测结果进行比较。结果表明:与其他模型相比,I-VS-PELM模型具有更高的预测精度和更好的泛化性能,能更准确地预测锅炉飞灰含碳量。
To accurately predict the carbon content in boiler fly ash, an improved vortex search(I-VS) algorithm was proposed based on Bloch coordinates and iterative chaos mapping, following which, a performance comparison was conducted among the original vortex search(VS) algorithm, I-VS algorithm, particle swarm optimization(PSO) algorithm, sine cosine algorithm(SCA) and salp swarm algorithm(SSA). Based on the operation data of a 300 MW circulating fluidized bed boiler, the I-VS algorithm was used to optimize the extreme learning machine with parallel layer perception(PELM) to form the I-VS-PELM model for prediction of carbon content in fly ash. The prediction results of I-VS-PELM were then compared with those of PELM, PSO-PELM, SCA-PELM, SSA-PELM and VS-PELM. Results show that, compared with other models, the I-VS-PELM model has not only higher prediction precision, but also better generalization ability, which could then be used to effectively predict the carbon content in boiler fly ash.
To accurately predict the carbon content in boiler fly ash, an improved vortex search(I-VS) algorithm was proposed based on Bloch coordinates and iterative chaos mapping, following which, a performance comparison was conducted among the original vortex search(VS) algorithm, I-VS algorithm, particle swarm optimization(PSO) algorithm, sine cosine algorithm(SCA) and salp swarm algorithm(SSA). Based on the operation data of a 300 MW circulating fluidized bed boiler, the I-VS algorithm was used to optimize the extreme learning machine with parallel layer perception(PELM) to form the I-VS-PELM model for prediction of carbon content in fly ash. The prediction results of I-VS-PELM were then compared with those of PELM, PSO-PELM, SCA-PELM, SSA-PELM and VS-PELM. Results show that, compared with other models, the I-VS-PELM model has not only higher prediction precision, but also better generalization ability, which could then be used to effectively predict the carbon content in boiler fly ash.
引文
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[3]赵健,袁瀚,梅宁.燃煤锅炉飞灰含碳量的BP神经网络模型[J].热科学与技术,2016,15(6):499-504.ZHAO Jian,YUAN Han,MEI Ning.BP neural network model of fly ash carbon content of coal-fired boiler[J].Journal of Thermal Science and Technology,2016,15(6):499-504.
[4]张正友,钱家俊,冯旭刚.基于蚁群神经网络的飞灰含碳量测量方法[J].计测技术,2017,37(1):18-20.ZHANG Zhengyou,QIAN Jiajun,FENG Xugang.Prediction method of unburned carbon content in fly ash based on ant colony algorithm[J].Metrology&Measurement Technology,2017,37(1):18-20.
[5]TAVARES L D,SALDANHA R R,VIEIRA D AG.Extreme learning machine with parallel layer perceptrons[J].Neurocomputing,2015,166:164-171.
[6]DOGAN B,?LMEZ T.A new metaheuristic for numerical function optimization:vortex search algorithm[J].Information Sciences,2015,293:125-145.
[7]POLI R,KENNEDY J,BLACKWELL T.Particle swarm optimization:an overview[J].Swarm Intelligence,2007,1(1):33-57.
[8]MIRJALILI S.SCA:a sine cosine algorithm for solving optimization problems[J].Knowledge-Based Systems,2016,96:120-133.
[9]MIRJALILI S,GANDOMI A H,MIRJALILI S Z,et al.Salp swarm algorithm:a bio-inspired optimizer for engineering design problems[J].Advances in Engineering Software,2017,114:163-191.
[10]TOGAN V,DALOGLU A T.An improved genetic algorithm with initial population strategy and self-adaptive member grouping[J].Computers&Structures,2008,86(11/12):1204-1218.
[11]LI Panchi.A quantum-behaved evolutionary algorithm based on the Bloch spherical search[J].Communications in Nonlinear Science and Numerical Simulation,2014,19(4):763-771.
[12]BOUSHAKI S I,KAMEL N,BENDJEGHABA O.A new quantum chaotic cuckoo search algorithm for data clustering[J].Expert Systems with Applications,2018,96:358-372.
[13]HE Di,HE Chen,JIANG Lingge,et al.Chaotic characteristics of a one-dimensional iterative map with infinite collapses[J].IEEE Transactions on Circuits and Systems I:Fundamental Theory and Applications,2001,48(7):900-906.
[14]林开平.人工神经网络的泛化性能与降水预报的应用研究[D].南京:南京信息工程大学,2007.
[15]李俭川,秦国军,温熙森,等.神经网络学习算法的过拟合问题及解决方法[J].振动、测试与诊断,2002,22(4):260-264.LI Jianchuan,QIN Guojun,WEN Xisen,et al.O-ver-fitting in neural network learning algorithms and its solving strategies[J].Journal of Vibration,Measurement&Diagnosis,2002,22(4):260-264.
[16]江学军,唐焕文.前馈神经网络泛化性能力的系统分析[J].系统工程理论与实践,2000,20(8):36-40.JIANG Xuejun,TANG Huanwen.System analysis for generalization of MFNN[J].Systems EngineeringTheory&Practice,2000,20(8):36-40.