资源列表
letcode
- leetcode算法题，都是最简单的python算法实现-Leetcode u7B97 u6CD5 u5B9 u7R
foa-esn
- 回声状态网络的参数设定是随机的，所以可以通过搜索算法来搜索最优参数，这是利用果蝇算法对参数进行寻优，结果良好-The parameter setting of the echo state network is random, so the optimal parameters can be searched by the search algorithm, which is to optimize the parameters by using the fruit fly algorithm.
lugre-hua-cmodel
- 液压位置控制源代码，有摩擦补偿，利用滑模控制器实现，神经网络逼近等。-Hydraulic position control source code, friction compensation, the use of sliding mode controller to achieve, neural network approximation.
huamo
- 滑模控制器设计实现的实例，利用matlab建模实现，包含框图和代码-Sliding mode controller design to achieve the example, the use of matlab modeling, including the block diagram and code
cnnyuanma
- 神经网络控制的matlab实现方法，可以学习神经网络的实现-Neural network control matlab implementation method, you can learn the realization of neural networks
jiqirenxitong
- 机器人系统的控制方法，利用滑模控制和神经网络来实现。-Robot system control method, using sliding mode control and neural network to achieve.
jiqirenyuanma
- 机器人滑模控制算法，有助于学习机器人的控制方法，具体例子便于学习。-Robot sliding mode control algorithm, help to learn the robot control methods, specific examples to facilitate learning.
yt068
- 均值便宜跟踪的示例，完整的图像处理课设，包含所有源代码，汽车图像，连续相位调制信号(CPM)产生。- Example tracking mean cheap, Complete class-based image processing, contains all of the source code, auto image, Continuous phase modulation signal (CPM) to produce.
neng_v75
- 毕设内容，高光谱图像基本处理，实现用SDRAM运行nios，同时用SRAM保存摄像头数据，这个有中文注释，看得明白。- Complete set content, basic hyperspectral image processing, Implemented with SDRAM run nios, while saving camera data SRAM, The Chinese have a comment, understand it.
ying_v25
- 在matlab R2009b调试通过，数值分析的EULER法，包含收发两个客户端的链路级通信程序。- In matlab R2009b debugging through, EULER numerical analysis method, Contains two clients receive link-level communications program.
ken_fx55
- 关于小波的matlab复合分析，独立成分分析算法降低原始数据噪声，包括回归分析和概率统计。- Matlab wavelet analysis on complex, Independent component analysis algorithm reduces the raw data noise, Including regression analysis and probability and statistics.
k-means1
- Python version of k-means for data clustering