20060409222636964

RD_高速布線技巧 is useful.rar(RD_ high-speed routing techniques is useful.rar)

Calculates SER in 64PSK,64QAM modulation technique and produces graphical figures for diversity order up to eight,rayleigh channel,bessel first order

遗传算法Matlab源代码,优化目标函数比较广泛，是常用的算法之一，同时也是学习其他算法的基础。(Genetic algorithm (ga) )

mil_188_9B_12A signal model

说明：  灰色关联分析法的Matlab源代码，可以实现最优化方案的选择(Grey relational analysis Matlab source code, the program can optimize the choice of)

MATLAB 信号处理 带通、高通、低通滤波 FFT变换 显示并返回原始波形的FFT结果、滤波后的波形及滤波后的FFT结果(MATLAB signal processing band-pass, high pass, low pass filter FFT of the waveform display and return to the original results of the FFT, the filtered waveform and the filtered results of the FFT)

Matlab Curve Fitting Document

这是TD算法的逆过程，首先将时间序列，划分为相邻点的短序列，当然此时的拟合误差为0，因为第一点和第二点的连线，原始点都落在线段上。将相邻两个线段连接起来，此时每条线段包含三个原始点，计算中间那个点的拟合误差。这样，所有这些三个点的线段中的中间点的拟合误差计算出来后，找出误差最小且误差小于阀值R的分段，作为第一条包含三个点的线段。 在上面的基础上，第一条分段同样的和相邻线段连接，然后计算每一条分段的拟合误差，再找出误差最小且小于阀值R的分段，作为第二个分段。依次方式循环，直到所有分段的拟合误差都小于阀值R，分段结束。 (This is the reverse process of TD algorithm, the first time series is divided into short sequences adjacent dots, of course fitting error is zero at this time, because the first and second points of connection, the original point fall line on. The two adjacent segments are connected, then each segment contains three original point, the intermediate fitting error calculated that point. Thus, after all these segments fitting error of three points in the intermediate point calculated to find the minimum error and the error is less than the threshold R segment, as the first line contains three points. Based on the above, the first segment and the adjacent line segment connecting the same, and then calculate a fitting error for each segment, and then find the smallest error and less than the threshold R segment, as the second segment . Followed by a cyclic manner until all segments fitting error is less than the threshold R, segment ends.)

基于动态规划的检测前跟踪算法中理论检测概率的计算程序(Based on dynamic programming tracking algorithm in the theory of pre-test probability of detection calculation program)

deep learning一般是学习的层次结构，它也是有一定理论依据的，即模拟人脑的大脑皮层工作，因为大脑皮层的视觉区域也是分层次工作的，越底层的视觉皮层对那些底层特征就越敏感。综上所述，feature learning有这么多应用需求的驱动和生物神经理论上的支持，注定它能够在AI领域中发挥一定的作用。一些实验表明，有些feature learning学习到的特征几乎比所有其它的特征效果要好，比如本文中的ISA模型就是其中一个。(deep learning hierarchy of learning, it is also a certain theoretical basis that simulate the human brain' s cerebral cortex, visual areas of the cerebral cortex is hierarchical work, the more the bottom of the visual cortex is more sensitive to those underlying characteristics . In summary, feature learning so many application needs the support of the drive and biological neural theoretically doomed it to be able to play a certain role in the field of AI. Some experiments show that some feature learning learning to feature almost better than all the other features of the effect of the ISA model such as this is one.)