基于分类优化贝叶斯结构算法的篦冷机参数状态分析及其算法收敛性分析
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摘要
针对种群算法建立贝叶斯结构存在参数多、易陷入局部最优的问题,提出一种改进贝叶斯结构学习算法。该算法将候选结构分为优劣解集,利用师生交流机制优化优解集保留精英个体,利用变异机制优化劣解集来增加结构多样性,从而加快算法收敛速度,并在准确率和运行时间上达到平衡。最后不仅利用马尔科夫链证明该算法是全局收敛的,而且通过仿真实验验证了所提出算法的性能。将该算法应用到水泥篦冷机的实际数据中,构建水泥篦冷机工艺参数的贝叶斯网络结构,并完成篦冷机参数状态分析。
Aiming at the problem that population algorithms of Bayesian structure learning have many parameters and easily fall into local optimum,an improved Bayesian structure learning algorithms is proposed. The algorithm combines the advantage of teaching learning based optimization without parameters and the random search of mutation mechanism.Teaching learning based optimization and mutation mechanism contrapuntally optimize candidate structures to learn the best Bayesian network structure. Teaching learning based optimization optimizes excellent set of structures to preserve entity.Mutation mechanism optimizes poor set of structures to increase structural diversity. By these operations,this algorithm not only accelerates the convergence speed,but also the balance between solutions quality and computational effort. Finally,the convergence of the algorithm is analyzed by Markov chain. The simulation results have shown that these properties can be achieved.
Aiming at the problem that population algorithms of Bayesian structure learning have many parameters and easily fall into local optimum,an improved Bayesian structure learning algorithms is proposed. The algorithm combines the advantage of teaching learning based optimization without parameters and the random search of mutation mechanism.Teaching learning based optimization and mutation mechanism contrapuntally optimize candidate structures to learn the best Bayesian network structure. Teaching learning based optimization optimizes excellent set of structures to preserve entity.Mutation mechanism optimizes poor set of structures to increase structural diversity. By these operations,this algorithm not only accelerates the convergence speed,but also the balance between solutions quality and computational effort. Finally,the convergence of the algorithm is analyzed by Markov chain. The simulation results have shown that these properties can be achieved.
引文
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[4] Mcnally R J,Heeren A,Robinaugh D J. A Bayesian network analysis of posttraumatic stress disorder symptoms in adults reporting childhood sexual abuse[J]. European Journal of Psychotraumatology,2017,8:1341276.
[5] Yang C,Ji J,Liu J,et al. Structural Learning of Bayesian Networks by Bacterial Foraging Optimization[J]. International Journal of Approximate Reasoning,2016,6(9):147-167.
[6] Larranaga P,Poza M,Yurramendi Y,et al. Structure learning of bayesian networks by genetic algorithms:a performance analysis of control parameters[J]. IEEE Transactions on Pattern Analysis&Machine Intelligence,1996,18(9):912-926.
[7]刘浩然,吕晓贺,李轩,等.基于Bayesian改进算法的回转窑参数控制模型研究[J].仪器仪表学报,2015,36(7):1554-1561.Liu H R,LüX H,Li X,et al. A study on the fault diagnosis model of rotary kiln based on an improved algorithm of Bayesian[J]. Chinese Jourmal of Scientific Instrument,2015,36(7):1554-1561.
[8] Koopman R,Wang S. Mutual information based labelling and comparing clusters[J]. Scientometrics,2017,111(2):1157-1167.
[9] Chow C K,Liu C N. Approximating discrete probability distributions with dependence trees[J]. IEEE Transactions on Information Theory,1968,14(3):462-467.
[10]邸若海,高晓光,郭志高.基于改进BIC评分的贝叶斯网络结构学习[J].系统工程与电子技术,2017,39(2):437-444.Di R H, Gao X G, Guo Z G. Bayesian networks structure learning based on improved BIC scoring[J].Systems Engineering and Electronics,2017,39(2):437-444.
[11]李冰寒.基于蚁群优化的贝叶斯网结构学习算法[D].西安:西安电子科技大学,2011.
[12] Franois O C H,Leray P. BNT Structure Learning Package:Documentation and Experiments[R]. 2004.
[13] Lauritzen S L,Spiegelhalter D J. Local Computations with Probabilities on Graphical Structures andtheir Application to Expert Systems[J]. Journal of the Royal Statistical Society,1988,50(2):157-224.
[14] Alan M B. The Use of The BIC Set in The Characterization of Used Nuclear Fuel Assemblies by Nondestructive Assay[J]. Annals of Nuclear Energy,2014,66(4):31-50.
[15] Beinlich I A,Suermondt H J,Chavez R M,et al. The ALARM monitoring mystem:a case study with two probabilistic inference techniques for belief networks[J]. Lecture Notes in Medical Informatics,1989,38:247-256.
[16]刘彬,赵朋程,高伟,等.基于粒子群算法与连续型深度信念网络的水泥熟料游离氧化钙预测[J].计量学报,2018,39(3):420-424.Liu B,Zhao P C,Gao W,et al. Prediction of Cement f Ca O Based on Particle Swarm Optimization and Continuous Deep Belief Network[J]. Acta Metrologica Sinica,2018,39(3):420-424.
[17] Wang M Q,Liu B,Wen Y,et al. Numerical Simulation and Analytical Characterization of Heat Transfer between Cement Clinker and Air in Grate Cooler[J]. Journal of Chemical Engineering of Japan,2016,49(1):10-15.
[18] Wei S,Zheng C,Lin C. Multi-objective optimization of cooling air distributions of grate cooler with different clinker particles diameters and air chambers by genetic algorithm[J]. Science China Technological Sciences,2017,60(3):345-354.
[19] Sadinle M. Bayesian Estimation of Bipartite Matchings for Record Linkage[J]. Journal of the American Statistical Association,2017,112(518):1-35.