-
matlab
matlab 灰度图像处理去噪 边缘化问题的相关处理(matlab grey proessing )
- 2015-03-23 18:54:40下载
- 积分:1
-
download(7)
LIMITATION OF SWITCHING OVERVOLTAGES BY USE OF
TRANSMISSION LINE SURGE ARRESTERS
- 2014-01-20 06:25:59下载
- 积分:1
-
Enhancei.m.tar
mat lab code for image enhancement
- 2012-11-28 21:09:13下载
- 积分:1
-
ctrllab3.0
很优良的PID控制器设计仿真程序与模型,经过严格检验(Very good simulation of PID controller design procedures and models, to undergo a rigorous inspection)
- 2008-05-21 16:23:58下载
- 积分:1
-
SURFmex
matalab调用C程序实现的基于SURF算法的全景图像拼接(matalab call C SURF algorithm based Program for panoramic image stitching)
- 2011-05-03 08:30:54下载
- 积分:1
-
av_power
matlab下计算脑电信号矩阵平均功率并显示,信号采用列向量形式排列,简短有用(compute the average power of eeg matrix,short and useful)
- 2013-08-11 09:55:55下载
- 积分:1
-
Arithmetic_Coding_and_Decoding
encoding and decoding based on arithmatic operators may be used for compression
- 2011-11-17 12:15:02下载
- 积分:1
-
WindyGridWorldQLearning
Q-learning (Watkins, 1989) is a simple way for agents to learn how to act optimally in controlled Markovian
domains. It amounts to an incremental method for dynamic programming which imposes limited computational
demands. It works by successively improving its evaluations of the quality of particular actions at particular states.
This paper presents and proves in detail a convergence theorem for Q,-learning based on that outlined in Watkins
(1989). We show that Q-learning converges to the optimum action-values with probability 1 so long as all actions
are repeatedly sampled in all states and the action-values are represented discretely. We also sketch extensions
to the cases of non-discounted, but absorbing, Markov environments, and where many Q values can be changed
each iteration, rather than just one.
- 2013-04-19 14:23:35下载
- 积分:1
-
vcPP6.0-and-matlab
matlab和vc++联合运行的方法介绍。。。。。(Matlab and vc++ combined operation method is introduced
)
- 2013-04-24 14:37:18下载
- 积分:1
-
LDPC_matlab
ldpc编码的matlab例子,比较详细,具有很高的价值(matlab coding ldpc example, more detailed, with a very high value)
- 2021-04-18 15:08:51下载
- 积分:1