In this paper, a novel Bayesian-Gaussian neural network (BGNN) is proposed and applied to on-line modeling of a hydraulic turbine system (HTS). The new BGNN takes account of the complex nonlinear characteristics of HT...In this paper, a novel Bayesian-Gaussian neural network (BGNN) is proposed and applied to on-line modeling of a hydraulic turbine system (HTS). The new BGNN takes account of the complex nonlinear characteristics of HTS. Two redefined training procedures of the BGNN include the off-line training of the threshold matrix parameters, optimized by swarm optimiza- tion algorithms, and the on-line BGNN predictive application driven by the sliding window data method. The characteristics models of an HTS are identified using the new BGNN method and simulation results are presented which show the effectiveness of the BGNN in addressing modeling problems of HTS.展开更多
两反式光学系统广泛应用于空间遥感、探测制导等领域,其成像质量是光学系统的核心指标,不仅依赖光学器件的制造精度,而且很大程度上受装配精度的影响。在实际工程中,光学系统装配后的成像质量很容易受到界面条件、装配位姿偏差等多源不...两反式光学系统广泛应用于空间遥感、探测制导等领域,其成像质量是光学系统的核心指标,不仅依赖光学器件的制造精度,而且很大程度上受装配精度的影响。在实际工程中,光学系统装配后的成像质量很容易受到界面条件、装配位姿偏差等多源不确定性因素的影响,即使相同的装配工艺参数也可能导致成像质量出现偏差。为此,提出一种两反式光学系统装配与成像的联合仿真方法,以能量集中度作为成像质量定量评价指标,辨识光学系统装配过程中的不确定性参数并进行不确定性度量,根据参数特点选择合理的采样方法,通过联合仿真方法得到不同装配误差条件下的光学系统成像质量数据。建立基于Matern5/2核函数的高斯过程回归(Gaussian Process Regression, GPR)拧紧力矩指向性代理模型,以及结合贝叶斯优化和蒙特卡洛模拟(Bayesian Optimization-Monte Carlo Simulation, BO-MCS)的不确定性优化算法,基于构建的原始数据集,实现光学系统装配不确定性建模分析与装配工艺参数鲁棒性优化。研究结果表明:与其他代理模型相比,所建立的GPR代理模型具有最小的成像质量预测误差(平均预测误差仅有1.95%);优化后的光学系统成像质量平均提升6.13%,波动半径平均减少14.05%,有效提高了光学系统装配后的成像质量一致性。展开更多
A Bayesian-Gaussian Neural Network (BGNN) is put forward in this paper to predict the static and transient performance of Circulating Fluidized Bed (CFB) boilers. The advantages of this network over Back-Propagation N...A Bayesian-Gaussian Neural Network (BGNN) is put forward in this paper to predict the static and transient performance of Circulating Fluidized Bed (CFB) boilers. The advantages of this network over Back-Propagation Neural Networks (BPNNs), easier determination of topology, simpler and time saving in training process as well as selforganizing ability, make this network more practical in on-line performance prediction for complicated processes. Simulation shows that this network is comparable to the BPNNs in predicting the performance of CFB boilers. Good and practical on-line performance predictions are essential for operation guide and model predictive control of CFB boiIers, which are under research by the authors.展开更多
基金Project (Nos. 60704024 and 60772107) supported by the National Natural Science Foundation of China
文摘In this paper, a novel Bayesian-Gaussian neural network (BGNN) is proposed and applied to on-line modeling of a hydraulic turbine system (HTS). The new BGNN takes account of the complex nonlinear characteristics of HTS. Two redefined training procedures of the BGNN include the off-line training of the threshold matrix parameters, optimized by swarm optimiza- tion algorithms, and the on-line BGNN predictive application driven by the sliding window data method. The characteristics models of an HTS are identified using the new BGNN method and simulation results are presented which show the effectiveness of the BGNN in addressing modeling problems of HTS.
文摘两反式光学系统广泛应用于空间遥感、探测制导等领域,其成像质量是光学系统的核心指标,不仅依赖光学器件的制造精度,而且很大程度上受装配精度的影响。在实际工程中,光学系统装配后的成像质量很容易受到界面条件、装配位姿偏差等多源不确定性因素的影响,即使相同的装配工艺参数也可能导致成像质量出现偏差。为此,提出一种两反式光学系统装配与成像的联合仿真方法,以能量集中度作为成像质量定量评价指标,辨识光学系统装配过程中的不确定性参数并进行不确定性度量,根据参数特点选择合理的采样方法,通过联合仿真方法得到不同装配误差条件下的光学系统成像质量数据。建立基于Matern5/2核函数的高斯过程回归(Gaussian Process Regression, GPR)拧紧力矩指向性代理模型,以及结合贝叶斯优化和蒙特卡洛模拟(Bayesian Optimization-Monte Carlo Simulation, BO-MCS)的不确定性优化算法,基于构建的原始数据集,实现光学系统装配不确定性建模分析与装配工艺参数鲁棒性优化。研究结果表明:与其他代理模型相比,所建立的GPR代理模型具有最小的成像质量预测误差(平均预测误差仅有1.95%);优化后的光学系统成像质量平均提升6.13%,波动半径平均减少14.05%,有效提高了光学系统装配后的成像质量一致性。
文摘A Bayesian-Gaussian Neural Network (BGNN) is put forward in this paper to predict the static and transient performance of Circulating Fluidized Bed (CFB) boilers. The advantages of this network over Back-Propagation Neural Networks (BPNNs), easier determination of topology, simpler and time saving in training process as well as selforganizing ability, make this network more practical in on-line performance prediction for complicated processes. Simulation shows that this network is comparable to the BPNNs in predicting the performance of CFB boilers. Good and practical on-line performance predictions are essential for operation guide and model predictive control of CFB boiIers, which are under research by the authors.
