针对暖通空调(HVAC)系统难以控制的问题,提出一种基于m ax-product推理的M am dan i模糊模型预测控制策略.首先利用一步模糊预测模型的结构分析得到其解析表达式,获得系统在k+1时刻的线性化预测模型;然后基于模糊线性化模型进行模型预...针对暖通空调(HVAC)系统难以控制的问题,提出一种基于m ax-product推理的M am dan i模糊模型预测控制策略.首先利用一步模糊预测模型的结构分析得到其解析表达式,获得系统在k+1时刻的线性化预测模型;然后基于模糊线性化模型进行模型预测控制器设计.对HVAC系统的仿真和实验结果表明,该算法是一种跟踪性能好且鲁棒性强的有效控制算法.展开更多
Deformation modulus is the important parameter in stability analysis of tunnels, dams and mining struc- tures. In this paper, two predictive models including Mamdani fuzzy system （MFS） and multivariable regression a...Deformation modulus is the important parameter in stability analysis of tunnels, dams and mining struc- tures. In this paper, two predictive models including Mamdani fuzzy system （MFS） and multivariable regression analysis （MVRA） were developed to predict deformation modulus based on data obtained from dilatometer tests carried out in Bakhtiary dam site and additional data collected from longwall coal mines. Models inputs were considered to be rock quality designation, overburden height, weathering, unconfined compressive strength, bedding inclination to core axis, joint roughness coefficient and fill thickness. To control the models performance, calculating indices such as root mean square error （RMSE）, variance account for （VAF） and determination coefficient （R^2） were used. The MFS results show the significant prediction accuracy along with high performance compared to MVRA results. Finally, the sensitivity analysis of MFS results shows that the most and the least effective parameters on deformation modulus are weatherin~ and overburden height, respectively.展开更多
文摘针对暖通空调(HVAC)系统难以控制的问题,提出一种基于m ax-product推理的M am dan i模糊模型预测控制策略.首先利用一步模糊预测模型的结构分析得到其解析表达式,获得系统在k+1时刻的线性化预测模型;然后基于模糊线性化模型进行模型预测控制器设计.对HVAC系统的仿真和实验结果表明,该算法是一种跟踪性能好且鲁棒性强的有效控制算法.
文摘Deformation modulus is the important parameter in stability analysis of tunnels, dams and mining struc- tures. In this paper, two predictive models including Mamdani fuzzy system （MFS） and multivariable regression analysis （MVRA） were developed to predict deformation modulus based on data obtained from dilatometer tests carried out in Bakhtiary dam site and additional data collected from longwall coal mines. Models inputs were considered to be rock quality designation, overburden height, weathering, unconfined compressive strength, bedding inclination to core axis, joint roughness coefficient and fill thickness. To control the models performance, calculating indices such as root mean square error （RMSE）, variance account for （VAF） and determination coefficient （R^2） were used. The MFS results show the significant prediction accuracy along with high performance compared to MVRA results. Finally, the sensitivity analysis of MFS results shows that the most and the least effective parameters on deformation modulus are weatherin~ and overburden height, respectively.
