In view of the difficulty in supporting the surrounding rocks of roadway 3-411 ofFucun Coal Mine of Zaozhuang Mining Group, a deformation forecasting model was putforward based on particle swarm optimization.The kerne...In view of the difficulty in supporting the surrounding rocks of roadway 3-411 ofFucun Coal Mine of Zaozhuang Mining Group, a deformation forecasting model was putforward based on particle swarm optimization.The kernel function and model parameterswere optimized using particle swarm optimization.It is shown that the forecast result isvery close to the real monitoring data.Furthermore, the PSO-SVM (Particle Swarm Optimization-Support Vector Machine) model is compared with the GM(1,1) model and L-M BPnetwork model.The results show that PSO-SVM method is better in the aspect of predictionaccuracy and the PSO-SVM roadway deformation pre-diction model is feasible for thelarge deformation prediction of coal mine roadway.展开更多
机械制造与装配车间电力需求的精准预测对合理安排机械生产加工、减少不必要的电能储备损耗有着重要意义。本文给出一种基于灰色理论优化vlPSO-LSSVM (variable linear Particle Swarm Optimization-Least Squares Support Vector Machi...机械制造与装配车间电力需求的精准预测对合理安排机械生产加工、减少不必要的电能储备损耗有着重要意义。本文给出一种基于灰色理论优化vlPSO-LSSVM (variable linear Particle Swarm Optimization-Least Squares Support Vector Machine)策略的电力储备需求预测模型。首先将预测的电力需求曲线通过滑动窗口将其划分为多个样本序列,结合灰色线性预测和支持向量机非线性映射快速精准的优势,在短期训练集内同时输出灰色预测序列和vlPSO-LSSVM预测序列;再定义训练规则,以一天(96个点)为一个周期,当周期中任意滑动窗内灰色预测序列不在LSSVM预测序列的包络线内时,这一滑动窗选用LSSVM预测序列作为预测输出,否则采用灰色预测序列作为输出。之后通过工程实例对本文模型进行验证,并与经典长短期记忆神经网络模型、BPNN和AR-RBFNN的预测结果对比分析。结果表明,基于灰色vlPSO-LSSVM模型的预测精确度显著优于其他算法,对机械生产车间制定合理的电力储备计划有较好的参考价值。展开更多
基金Supported by the National Natural Science Foundation of Zhejiang Province(2009C33049)the National Natural Science Foundation of China(50674040)
文摘In view of the difficulty in supporting the surrounding rocks of roadway 3-411 ofFucun Coal Mine of Zaozhuang Mining Group, a deformation forecasting model was putforward based on particle swarm optimization.The kernel function and model parameterswere optimized using particle swarm optimization.It is shown that the forecast result isvery close to the real monitoring data.Furthermore, the PSO-SVM (Particle Swarm Optimization-Support Vector Machine) model is compared with the GM(1,1) model and L-M BPnetwork model.The results show that PSO-SVM method is better in the aspect of predictionaccuracy and the PSO-SVM roadway deformation pre-diction model is feasible for thelarge deformation prediction of coal mine roadway.
文摘机械制造与装配车间电力需求的精准预测对合理安排机械生产加工、减少不必要的电能储备损耗有着重要意义。本文给出一种基于灰色理论优化vlPSO-LSSVM (variable linear Particle Swarm Optimization-Least Squares Support Vector Machine)策略的电力储备需求预测模型。首先将预测的电力需求曲线通过滑动窗口将其划分为多个样本序列,结合灰色线性预测和支持向量机非线性映射快速精准的优势,在短期训练集内同时输出灰色预测序列和vlPSO-LSSVM预测序列;再定义训练规则,以一天(96个点)为一个周期,当周期中任意滑动窗内灰色预测序列不在LSSVM预测序列的包络线内时,这一滑动窗选用LSSVM预测序列作为预测输出,否则采用灰色预测序列作为输出。之后通过工程实例对本文模型进行验证,并与经典长短期记忆神经网络模型、BPNN和AR-RBFNN的预测结果对比分析。结果表明,基于灰色vlPSO-LSSVM模型的预测精确度显著优于其他算法,对机械生产车间制定合理的电力储备计划有较好的参考价值。
