为实现油田生产管理和决策的现代化,使地层参数估值具有全局最优性,在研究油井井底压力分布的描述和有关地层参数辨识问题的基础上,提出了一种由二阶学习算法与GA(Genetic A lgorithm)构成的新型混合遗传算法,并给出一种新型神经网络。...为实现油田生产管理和决策的现代化,使地层参数估值具有全局最优性,在研究油井井底压力分布的描述和有关地层参数辨识问题的基础上,提出了一种由二阶学习算法与GA(Genetic A lgorithm)构成的新型混合遗传算法,并给出一种新型神经网络。该网络把级数中的函数看成非线性神经元,建立油藏系统的函数型连接人工神经网络模型。由系统辨识理论中的F检验法确定网络模型的结构参数n,用二阶学习算法和新型GA交替辨识网络模型的权系数v和地层参数θ。应用表明,采用上述方法建模精度高,模型的平均相对误差在1%以内,并能求出地层参数的全局最优估值。展开更多
The design of a turbofan rotor speed control system, using model reference adaptive control（MRAC） method with input and output measurements, is discussed for the purpose of practical application. The nonlinear compe...The design of a turbofan rotor speed control system, using model reference adaptive control（MRAC） method with input and output measurements, is discussed for the purpose of practical application. The nonlinear compensator based on functional link neural network is used to deal with the engine nonlinearity and the hardware-in-loop simulation is also developed. The results show that the nonlinear MRAC controller has the adequate performance of compensating and adapting nonlinearity arising from the change of engine state or working environment. Such feature demonstrates potential practical applications of MRAC for aeroengine control system.展开更多
文摘为实现油田生产管理和决策的现代化,使地层参数估值具有全局最优性,在研究油井井底压力分布的描述和有关地层参数辨识问题的基础上,提出了一种由二阶学习算法与GA(Genetic A lgorithm)构成的新型混合遗传算法,并给出一种新型神经网络。该网络把级数中的函数看成非线性神经元,建立油藏系统的函数型连接人工神经网络模型。由系统辨识理论中的F检验法确定网络模型的结构参数n,用二阶学习算法和新型GA交替辨识网络模型的权系数v和地层参数θ。应用表明,采用上述方法建模精度高,模型的平均相对误差在1%以内,并能求出地层参数的全局最优估值。
文摘The design of a turbofan rotor speed control system, using model reference adaptive control（MRAC） method with input and output measurements, is discussed for the purpose of practical application. The nonlinear compensator based on functional link neural network is used to deal with the engine nonlinearity and the hardware-in-loop simulation is also developed. The results show that the nonlinear MRAC controller has the adequate performance of compensating and adapting nonlinearity arising from the change of engine state or working environment. Such feature demonstrates potential practical applications of MRAC for aeroengine control system.
