公开(公告)号：US20110125684A1

公开(公告)日：2011-05-26

申请号：US12591603

申请日：2009-11-24

Applicant: Hussain Al-Duwaish ,  Syed Z. Rizvi

Inventor： Hussain Al-Duwaish ,  Syed Z. Rizvi

IPC: G06N3/02

CPC classification number: G06N3/0481

Abstract: The method for the identifying of multiple input, multiple output (MIMO) Hammerstein models that includes modeling of the linear dynamic part of a Hammerstein model with a state-space model, and modeling the nonlinear part of the Hammerstein model with a radial basis function neural network (RBFNN).

Abstract translation: 用于识别多输入，多输出（MIMO）Hammerstein模型的方法包括用状态空间模型建立Hammerstein模型的线性动态部分，并用径向基函数神经元模拟Hammerstein模型的非线性部分 网络（RBFNN）。

公开(公告)号：US08595162B2

公开(公告)日：2013-11-26

申请号：US13215097

申请日：2011-08-22

Applicant: Hussain Al-Duwaish ,  Syed Zeeshan Rizvi

Inventor： Hussain Al-Duwaish ,  Syed Zeeshan Rizvi

IPC: G06E1/00 ,  G06E3/00 ,  G06F15/18 ,  G06G7/00

CPC classification number: G05B13/027

Abstract: The robust controller for nonlinear MIMO systems uses a radial basis function (RBF) neural network to generate optimal control signals abiding by constraints, if any, on the control signal or on the system output. The weights of the neural network are trained in the negative direction of the gradient of output squared error. Nonlinearities in the system, as well as variations in system parameters, are handled by the robust controller. Simulation results are included in the end to assess the performance of the proposed controller.

Abstract translation: 用于非线性MIMO系统的鲁棒控制器使用径向基函数（RBF）神经网络来产生遵守控制信号或系统输出上的约束（如果有的话）的最佳控制信号。 神经网络的权重在输出平方误差梯度的负方向进行训练。 系统中的非线性以及系统参数的变化由鲁棒控制器处理。 最后包括模拟结果以评估所提出的控制器的性能。

公开(公告)号：US20130262037A1

公开(公告)日：2013-10-03

申请号：US13438651

申请日：2012-04-03

Applicant: HUSSAIN AL-DUWAISH ,  ZAKARIYA AL-HAMOUZ ,  SYED ZEESHAN RIZVI

Inventor： HUSSAIN AL-DUWAISH ,  ZAKARIYA AL-HAMOUZ ,  SYED ZEESHAN RIZVI

IPC: G06F15/00

CPC classification number: G01R31/346 ,  G01R19/0053 ,  G01R31/1227

Abstract: The partial discharge noise separation method uses Independent Component Analysis (ICA) for de-noising partial discharge (PD) test signals having a noise signal component and a partial discharge component. Assuming that the noise signal component and the PD signal component are both statistically independent of each other and non-Gaussian, the ICA algorithm separates the noise component from the PD signal component from two partial discharge test signals acquired from two separate couplers per phase that are connected to the windings of a three-phase rotating machine.

Abstract translation: 局部放电噪声分离方法使用独立分量分析（ICA）去噪噪声信号分量和局部放电分量的局部放电（PD）测试信号。 假设噪声信号分量和PD信号分量在统计上彼此独立且非高斯两个，则ICA算法将噪声分量与PD信号分量与从每个相位两个分离的耦合器获得的两个局部放电测试信号分离， 连接到三相旋转机器的绕组。

公开(公告)号：US20130054500A1

公开(公告)日：2013-02-28

申请号：US13215097

申请日：2011-08-22

Applicant: HUSSAIN AL-DUWAISH ,  SYED ZEESHAN RIZVI

Inventor： HUSSAIN AL-DUWAISH ,  SYED ZEESHAN RIZVI

IPC: G06N3/02

CPC classification number: G05B13/027

Abstract: The robust controller for nonlinear MIMO systems uses a radial basis function (RBF) neural network to generate optimal control signals abiding by constraints, if any, on the control signal or on the system output. The weights of the neural network are trained in the negative direction of the gradient of output squared error. Nonlinearities in the system, as well as variations in system parameters, are handled by the robust controller. Simulation results are included in the end to assess the performance of the proposed controller.

Abstract translation: 用于非线性MIMO系统的鲁棒控制器使用径向基函数（RBF）神经网络来产生遵守控制信号或系统输出上的约束（如果有的话）的最佳控制信号。 神经网络的权重在输出平方误差梯度的负方向进行训练。 系统中的非线性以及系统参数的变化由鲁棒控制器处理。 最后包括模拟结果以评估所提出的控制器的性能。