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M=4
基于matlab的信噪比差错估计 m=4双极性(Matlab error signal to noise ratio is estimated based on m = 4 bipolar)
- 2010-12-07 20:57:17下载
- 积分：1
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hog_feature_vector
The given code finds the HOG feature vector for any given image. HOG
feature vector/descriptor can then be used for detection of any
particular object.
(The given code finds the HOG feature vector for any given image. HOG feature vector/descriptor can then be used for detection of any particular object.)
- 2015-01-10 21:04:26下载
- 积分：1
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kalmanFilter
卡尔曼滤波程序用于时间序列预测，结构简单程序易懂(kalman filter for prediction)
- 2021-01-21 17:28:48下载
- 积分：1
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DESHTM
用VHDL语言实现了DES加密算法，其中包含了测试程序，能够进行仿真。(Using VHDL language implementation of the DES encryption algorithm, which contains the test procedures can be simulated.)
- 2009-03-15 12:29:56下载
- 积分：1
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pca
用matlab 7.0 编的 主成分分析程序（GUI）(principal component analysis program （matlab7.0 GUI）)
- 2011-01-03 12:00:51下载
- 积分：1
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Fx
说明： ou should upload at least five sourcecodes/do
- 2010-03-25 04:20:33下载
- 积分：1
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OFDM_QAM
OFDM系统仿真Matlab程序（参数对照2K模式，映射使用4QAM）(OFDM system simulation Matlab program (2K mode control parameter mapping using 4QAM))
- 2015-04-06 19:46:42下载
- 积分：1
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Atttection
说明： 吸引子键波在弱信号检测中的设计Attractor in the design of weak signal detection(Attractor in the design of weak signal detection)
- 2011-04-04 12:22:03下载
- 积分：1
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channel-fade
衰落信道的仿真，包括瑞利，莱斯信道，还有分集接收等内容(Fading channel simulation, including the content of the Rayleigh and Rice channel, there is diversity reception)
- 2013-02-26 12:30:01下载
- 积分：1
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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
