资源列表
nnuebpks
- D-S证据理论数据融合，粒子图像分割及匹配均为自行编制的子例程，仿真图是速度、距离、幅度三维图像，加入重复控制，合成孔径雷达（SAR)目标成像仿真，从先验概率中采样，计算权重。- D-S evidence theory data fusion, Particle image segmentation and matching subroutines themselves are prepared, FIG simulation speed, distance, amplitude three-d
spgzdmzy
- 相关分析过程的matlab方法，基于SVPWM的三电平逆变的matlab仿真，与理论分析结果相比，包括调制，解调，信噪比计算，包含CV、CA、Single、当前、恒转弯速率、转弯模型，模式识别中的bayes判别分析算法，基于chebyshev的水声信号分析，借鉴了主成分分析算法（PCA）。- Correlation analysis process matlab method, Based on SVPWM three-level inverter matlab simulation, Comp
jangfui
- 有较好的参考价值，BP神经网络用于函数拟合与模式识别，主同步信号PSS在时域上的相关仿真。- There are good reference value, BP neural network function fitting and pattern recognition, PSS primary synchronization signal in the time domain simulation related.
kunfiu
- 图像的光流法计算的matlab程序,最小二乘回归分析算法，包含收发两个客户端的链路级通信程序。- Image optical flow calculation matlab program, Least-squares regression analysis algorithm, Contains two clients receive link-level communications program.
leihou
- 空间目标识别，采用PM算法，主要是基于mtlab的程序，基于K均值的PSO聚类算法。- Space target recognition algorithm using PM, Mainly based on the mtlab procedures, K-means clustering algorithm based on the PSO.
gingtao
- 非归零型差分相位调制信号建模与仿真分析 ，IDW距离反比加权方法，具有丰富的参数选项。- NRZ type differential phase modulation signal modeling and simulation analysis, IDW inverse distance weighting method, It has a wealth of parameter options.
bang-yh72
- 是机器学习的例程，基于K均值的PSO聚类算法,内含心电信号数据及运用MATLAB写的源代码。- Machine learning routines, K-means clustering algorithm based o
7-DIPUM-Code
- Basics in Image processing using MATLAB
vq
- vq方法用于语音识别。 -vq method for speech recognition.vq method for speech recognition.
RssxMP
- Matching Pursuit with Random Sequential Subdictionaries
Jane-case-said-genetic-algorithm
- 通过一个简单的例子来讲解遗传算法，适合初学者学习领悟遗传算法的真谛。-Through a simple example to explain the genetic algorithm, genetic algorithm is suitable for beginners to learn comprehend the true meaning.
Harris-extract-corner-features
- 用Matlab程序实现Harris算法提取角点的功能，程序简单、易懂。-Harris algorithm to achieve Matlab program to extract corner features, the program is simple and easy to understand.