融合多源求参结果的开采沉陷稳健预计方法
Steady Mining Subsidence Prediction Method by Fusing Multi-source Parameters Identification Results
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摘要: 为了克服单一求参模型由于求参结果不可靠、不精准造成的开采沉陷预计结果不稳健问题，重点研究了融合多源求参模型参数的开采沉陷稳健预计方法，并基于最小二乘原理推导了通用模型，构建了基于融合模矢法、遗传算法和模拟退火求参结果的开采沉陷预计新模型，并对模型的可行性进行了模拟实验验证。实验结果表明：新模型下沉预计误差及水平移动预计误差明显优于基于单一模矢法、遗传算法、模拟退火法反演的参数的下沉预计误差；融合多源求参模型参数的开采沉陷稳健预计方法具有一定抗差能力。Abstract: In order to overcome the problem of unreliability and inaccuracy results of the single parameter identification model leading to the unsteady of mining subsidence prediction results, steady mining subsidence prediction method by fusing multi-source parameter identification model is studied in this paper, a new model for mining subsidence prediction is established by integrating the parameters identification results of model vector method, genetic algorithm and simulated annealing based on the least square principle, and the feasibility of the model is simulated. The experimental results show that the subsidence error and horizontal displacement error of the new prediction model is better than the estimated error of parameters inversion based on single mode vector method, genetic algorithm, simulated annealing method; new prediction method by fusing multi-source mining subsidence prediction parameters identification model has a certain anti-error ability.
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