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FIR
用窗函数设计FIR 并用lattice实现滤波~！！！(fir)
- 2009-11-21 11:16:15下载
- 积分：1
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conv_dsp_1_exp
说明： 有关dsp的,有好多子程序,还是不错的,欢迎使用(the dsp, there were a lot subroutine, or good, welcomed the use of)
- 2006-04-28 10:43:31下载
- 积分：1
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load_flow
calculation of load flow in power system using matlab
power engineering field
- 2013-07-18 16:59:20下载
- 积分：1
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fangchafenxi
利用离差分解法计算出方差分析表，并用Ｆ检验法，检查对给定的显著性水平 ，因子Ａ对实验数据是否有限著影响(Use from the Finite Difference Method to calculate the analysis of variance table and F test, to check a given significance level, factor A is limited to whether the experimental data )
- 2020-10-20 20:07:24下载
- 积分：1
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Example_PLSR
PLS方法的回归分析，PLSR
参考文献：Partial Least Squares (PLS) methods for neuroimaging: A tutorial and review，NeuroImg，2014，(PLSR:Partial Least Squares Regresion
Partial Least Squares (PLS) methods for neuroimaging: A tutorial and review，NeuroImg，2014，)
- 2015-01-26 16:31:12下载
- 积分：1
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Erlang-B
仿真采用Erlang B模型，此仿真考虑用户呼叫时，若无空闲信道则被阻塞的情况。对话务量和阻塞率进行仿真(Simulation using Erlang B model, consider the user calls this simulation, the channel was idle without congestion. Blocking rate for traffic simulation )
- 2012-01-05 22:25:14下载
- 积分：1
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dierzhang2
matlab课件很有用的,对matlab学习很有帮助,希望采纳谢谢(it s very useful)
- 2014-01-24 10:59:41下载
- 积分：1
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particle filter prediction
利用粒子滤波算法实现预测，算法可直接运行(particle filter prediction)
- 2018-01-10 23:09:30下载
- 积分：1
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horn
field distribution of a horn antenna 2D
- 2011-10-01 17:56:23下载
- 积分：1
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elm_example
极限学习机（extreme learning machine）ELM是一种简单易用、有效的单隐层前馈神经网络SLFNs学习算法。2006年由南洋理工大学黄广斌副教授提出。传统的神经网络学习算法（如BP算法）需要人为设置大量的网络训练参数，并且很容易产生局部最优解。极限学习机只需要设置网络的隐层节点个数，在算法执行过程中不需要调整网络的输入权值以及隐元的偏置，并且产生唯一的最优解，因此具有学习速度快且泛化性能好的优点。(Extreme Learning Machine (extreme learning machine) ELM is an easy-to-use and effective single hidden layer feedforward neural network the SLFNs learning algorithm. 2006 by the Nanyang Technological University Associate Professor Huang Guangbin. Traditional neural network learning algorithm (BP) artificial network training parameters, and it is easy to generate a local optimal solution. Extreme Learning Machine network only need to set the number of hidden nodes, the algorithm implementation process does not need to adjust the network input weights and hidden element of bias, and only optimal solution, so the learning speed and generalization good performance advantages.)
- 2013-03-29 13:05:47下载
- 积分：1
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