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All_OptionPricing_Codes
Matlab Algos for Option Pricing
- 2009-09-12 01:33:45下载
- 积分：1
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BPSK
BPSK解调中频偏检测器的设计与仿真,内含具体的PPT文档说明(BPSK demodulation frequency offset detector design and simulation, containing a specific PPT documentation)
- 2009-09-21 19:17:00下载
- 积分：1
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Matlab-mathematical-application
matlab 数学应用，讲解如何用matlab求数学极限，倒数，积分，微分等数学运算(Matlab mathematical application)
- 2012-03-28 21:05:21下载
- 积分：1
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ejh42a
example.. of class.. control..
- 2012-06-23 03:43:00下载
- 积分：1
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RM_code
1、本程序用于仿真里德-穆勒（Reed-Muller）码在AWGN信道中的性能，调制采用bpsk
2、理论请参见《差错控制编码》（第二版），Shu Lin（美）编，晏坚等（译）一书P70-75.且仿真结果与书中图4-2完全一致
3、BPSK_AWGN_RM_Code.m为主程序，点击运行即可
4、The_creation_of_RM_code.m和RM_Decode.m分别为RM码的编、译码程序，而Majority_logic_decision.m用于译码中的大数判决，combine_dunction.m用于计算组合数。(1, the procedure for the simulation Reed- Muller (Reed-Muller) codes in AWGN channel performance, modulation using bpsk 2, theory, see " Error Control Coding" (second edition), Shu Lin (America) series , Yan Jian, etc. (translated) book P70-75., and the simulation results consistent with the book Figure 4-2 3, BPSK_AWGN_RM_Code.m-based program, click Run to 4, The_creation_of_RM_code.m and RM_Decode.m were RM compiled code decoding process, and in large numbers for decoding Majority_logic_decision.m judgment, combine_dunction.m used to calculate the number of combinations.)
- 2013-12-09 20:45:48下载
- 积分：1
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sldemo_msfcn_edge_detect
针对图像的边缘检测的SIMULINK模型,可以准实时处理(against the edge of the image detection simulation model will permit real-time processing)
- 2006-12-18 22:53:53下载
- 积分：1
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viterbi_trellis_generator
viterbi trellis generator
- 2010-01-20 00:23:53下载
- 积分：1
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elm112
it s un neural network Elman with train sim..
- 2011-05-15 22:03:19下载
- 积分：1
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RS232-Communication
RS232 comunication by Matlab GUI
- 2010-11-25 16:20:40下载
- 积分：1
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ZCR
autocov computes the autocovariance between two column vectors X and Y with same length N using the Fast Fourier Transform algorithm from 0 to N-2.
The resulting autocovariance column vector acv is given by the formula:
acv(p,1) = 1/(N-p) * sum_{i=1}^{N}(X_{i} - X_bar) * (Y_{i+p} - Y_bar)
where X_bar and Y_bar are the mean estimates:
X_bar = 1/N * sum_{i=1}^{N} X_{i} Y_bar = 1/N * sum_{i=1}^{N} Y_{i}
It satisfies the following identities:
1. variance consistency: if acv = autocov(X,X), then acv(1,1) = var(X)
2. covariance consistence: if acv = autocov(X,Y), then acv(1,1) = cov(X,Y)
- 2013-05-26 22:12:50下载
- 积分：1
