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| Parameter state analysis of grate cooler based on Bayesian structure algorithm based on classification optimization& and convergence analysis |
| LIU Hao-ran1,2,SUN Mei-ting1,2,WANG Hai-yu1,2,ZHANG Li-yue1,2,FAN Rui-xing1,2,LIU Bin1,2 |
1. Hebei Province Key Laboratory of Special Optical Fiber & Optical Fiber Sensing, Qinhuangdao, Hebei 066004, China
2. Electrical Engineering College of Yanshan University, Qinhuangdao, Hebei 066004, China |
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Abstract 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.
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Received: 30 January 2018
Published: 10 June 2019
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2019, Vol. 40 
