多输入多输出系统example_mimo

说明：  无刷双馈电机建模仿真的matlab、simulink代码。控制绕组的频率一定时，而发电机的转速在风速下的变化过程。 (BDFM Modeling and Simulation of matlab, simulink code. Control winding frequency of a certain time, while the generator speed in the wind under the process of change.)

说明：  matlab中的傅里叶变换，线性卷积，循环卷积，在基带等效信号加高斯白噪声信道模型下，BPSK,QPSK,QAM等调制解调的误码率计算。(Do not call any function in the case, resulting in [0,1]uniform distribution of interval random variable matlab program.)

growcut algorithm is implemented for segmentation purpose

用matlab实现预测控制中的GPC算法(Predictive control with matlab implementation of the GPC algorithm)

nlms算法的实验，仿真，在白噪声下的情况(nlms is simulated on matlab)

用MATLAB实现灰色预测GM(1，1)模型(Gray prediction GM (1,1) model in MATLAB)

a function to perform a simple 1D SAR simulation

Power system stabilizers (PSS) are now routinely used in the industry to damp out power system oscillations. In this paper, real-coded genetic algorithm (RCGA) optimization technique is applied to design robust power system stabilizer for both singlemachine infinite-bus (SMIB) and multi-machine power system. The design problem of the proposed controller is formulated as an optimization problem and RCGA is employed to search for optimal controller parameters. By minimizing the time-domain based objective function, in which the deviation in the oscillatory rotor speed of the generator is involved stability performance of the system is improved. The non-linear simulation results are presented under wide range of operating conditions disturbances at different locations as well as for various fault clearing sequences to show the effectiveness and robustness of the proposed controller and their ability to provide efficient damping of low frequency oscillations.

电力系统稳定性-暂态稳定性Power system stability - transient stability(Power system stability- transient stability Power system stability- transient stability)

流形学习算法LTSA的线性化方法，在基因分类聚类中得到了应用，可以将新样本线性地投射到低维空间。(LTSA manifold learning algorithm of the linearization method, clustering in gene classification has been applied to new samples can be projected onto the linear and low-dimensional space.)