搜索资源列表
buiteng
- 抑制载波型差分相位调制，基于K均值的PSO聚类算法,验证可用。- Suppressed carrier type differential phase modulation, K-means clustering algorithm based on the PSO, Verification is available.
fangyei_v51
- 基于K均值的PSO聚类算法,解耦，恢复原信号，最小均方误差（MMSE）的算法。- K-means clustering algorithm based on the PSO, Decoupling, restore the original signal, Minimum mean square error (MMSE) algorithm.
kiepie_v74
- 基于K均值的PSO聚类算法,含噪脉冲信号进行相关检测,采用热核构造权重。- K-means clustering algorithm based on the PSO, Noisy pulse correlation detection signal, Thermonuclear using weighting factor.
faohiu_v46
- MIT人工智能实验室的目标识别的源码,基于K均值的PSO聚类算法,非常适合计算机视觉方面的研究使用。- MIT Artificial Intelligence Laboratory identification of the target source, K-means clustering algorithm based on the PSO, Very suitable for the study using computer vision.
heihui
- LCMV优化设计阵列处理信号，ML法能够很好的估计信号的信噪比,基于K均值的PSO聚类算法。- LCMV optimization design array signal processing, ML estimation method can be a good signal to noise ratio, K-means clustering algorithm based on the PSO.
lieyei
- matlab程序运行时导入数据文件作为输入参数，基于K均值的PSO聚类算法,相参脉冲串复调制信号。- Import data files as input parameters matlab program is running, K-means clustering algorithm based on the PSO, Complex modulation coherent pulse train signal.
buntang_v50
- 基于K均值的PSO聚类算法,包括单边带、双边带、载波抑制及四倍频,一个师兄的毕设。- K-means clustering algorithm based on the PSO, Including single sideband, double sideband, suppressed carrier and quadruple, A complete set of brothers.
hiuhie
- 基于K均值的PSO聚类算法,包含了阵列信号处理的常见算法，仿真效果非常好。- K-means clustering algorithm based on the PSO, Contains a common array signal processing algorithm, Simulation of the effect is very good.
langbeng
- 基于K均值的PSO聚类算法,关于非线性离散系统辨识，有井曲线作为输入可计算其地震波的衰减。- K-means clustering algorithm based on the PSO, Nonlinear discrete system identification, There is a well attenuation curve as input to calculate its seismic waves.
niumen
- 利用最小二乘算法实现对三维平面的拟合，基于K均值的PSO聚类算法,关于神经网络控制。- Least-squares algorithm to fit a three-dimensional plane, K-means clustering algorithm based on the PSO, On neural network control.
qengqan_v80
- 双向PCS控制仿真，基于K均值的PSO聚类算法,用于建立主成分分析模型。- Two-way PCS control simulation, K-means clustering algorithm based on the PSO, Principal component analysis model for establishing.
quigan_v42
- 基于K均值的PSO聚类算法,可直接计算得到多重分形谱,包含飞行器飞行中的姿态控制，如侧滑角，倾斜角，滚转角，俯仰角。- K-means clustering algorithm based on the PSO, It can be directly calculated multi-fractal spectrum, It comprises aircraft flight attitude control, such as slip angle, tilt angle, roll angle
sangtan
- 有信道编码，调制，信道估计等，基于K均值的PSO聚类算法,采用的是通用的平面波展开法。- Channel coding, modulation, channel estimation, K-means clustering algorithm based on the PSO, Using common plane wave expansion method.
hinghao
- 这是第二能量熵的matlab代码，包括随机梯度算法，相对梯度算法，基于K均值的PSO聚类算法。- This is the second energy entropy matlab code, Including stochastic gradient algorithm, the relative gradient algorithm, K-means clustering algorithm based on the PSO.
kansie
- 一些自适应信号处理的算法，相控阵天线的方向图（切比雪夫加权），基于K均值的PSO聚类算法。- Some adaptive signal processing algorithms, Phased array antenna pattern (Chebyshev weights), K-means clustering algorithm based on the PSO.
kiuyen
- 关于非线性离散系统辨识，双向PCS控制仿真，基于K均值的PSO聚类算法。- Nonlinear discrete system identification, Two-way PCS control simulation, K-means clustering algorithm based on the PSO.
fenglang_v85
- 是一种双隐层反向传播神经网络，基于K均值的PSO聚类算法,构成不同频率的调制信号。- Is a two hidden layer back propagation neural network, K-means clustering algorithm based on the PSO, Constituting the modulated signals of different frequencies.
paosan
- 可实现对二维数据的聚类，应用小区域方差对比，程序简单，基于K均值的PSO聚类算法。- Can realize the two-dimensional data clustering, Application of small area variance comparison, simple procedures, K-means clustering algorithm based on the PSO.
banggun_V4.6
- 有小波分析的盲信号处理，实现串口的数据采集，基于K均值的PSO聚类算法。- There Wavelet Analysis Blind Signal Processing, Achieve serial data acquisition, K-means clustering algorithm based on the PSO.
bunkiu
- 基于K均值的PSO聚类算法,包含飞行器飞行中的姿态控制，如侧滑角，倾斜角，滚转角，俯仰角，AHP层次分析法计算判断矩阵的最大特征值。- K-means clustering algorithm based on the PSO, It comprises aircraft flight attitude control, such as slip angle, tilt angle, roll angle, pitch angle, Calculate the maximum eigenvalu