根据YANN LECUN的论文《Gradient-based Learning Applied To Document Recognition》设计的LeNet-5神经网络，C语言写成，不依赖任何第三方库。 MNIST手写字符集初代训练识别率97%，多代训练识别率98%。搬运至GitHub的VS工程。
According To YANN LECUN's paper gradient-based Learning Applied To Document Recognition, the lenet-5 neural network designed is written in C language and does not rely on any third-party library.The initial training recognition rate of MNIST handwritten character set is 97%, and the multi-generation training recognition rate is 98%.VS project on GitHub (2020-03-11, C/C++, 11255KB, 下载5次)
ldpc码的比特反转译码算法利用C语言实现，虽然简单但比较完整
The bit inversion decoding algorithm of LDPC code is implemented in C language, which is simple but complete. (2019-05-06, C/C++, 1KB, 下载2次)
本侧率主要运用在外汇投资上市用特别欧美品种起到非常不错稳定效果大家可以下载自己进行研究一下。
This side rate is mainly used in foreign exchange investment listing with special European and American varieties play a very good stabilization effect you can download their own research. (2018-10-25, C/C++, 58KB, 下载16次)
lattice boltzmann method一书中附录1的程序
lattice boltzmann method code 1 (2016-09-30, C/C++, 1KB, 下载1次)
BOARD_XMEGA_A1_XPLAINED & RFM23BP
BOARD_XMEGA_A1_XPLAINED & RFM23BP (2014-07-03, C/C++, 15975KB, 下载19次)
这是bf求最长公共子列，由于算法比较简单，就是简单的遍历，下次有功夫，把动态规划的算法上传。
this is just a simple lsc problem. (2009-05-10, C/C++, 1KB, 下载36次)
对具有随机噪声的二阶系统的模型辨识，进行标幺化以后系统的参考模型差分方程为： y(k)=a1*y(k-1)+a2*y(k-2)+b*u(k-1)+s(k) 式中，a1=0.3366,a2=0.6634,b=0.68,s(k)为随机噪声。由于神经网络的输出最大为1，所以，被辨识的系统应先标幺化，这里标幺化系数为5。采用正向建模(并联辨识)结构，神经网络选用3-9-9-1型，即输入层i，隐层j包括2级，输出层k的节点个数分别为3、9、9、1个；由于神经网络的最大输出为1，因此在辨识前应对原系统参考模型标么化处理，辨识结束后再乘以标么化系数才是被辨识系统的辨识结果。
of random noise with the second-order system model, per-unit system after the reference model differential equation : y (k) = y* a1 (k-1) a2* y (k-2) b* u (k-1) s (k)- style, = 0.3366 a1, a2 = 0.6634, b = 0.68, s (k) as random noise. Because the neural network for a maximum output, therefore, the identification system should be per-unit, per-unit here coefficient of 5. Forward modeling (Parallel identification) structure, neural network-based selection 3-9-9-1, i input layer, hidden layer, including two j, k output layer to the number of nodes 3,9,9,1; The neural network the biggest losers up to one, in the original deal before Identification System Reference Model S Mody treatment, then multiplied by the end of Identification Standard Mody coefficient was recognition system is the ide (2005-06-06, C/C++, 854KB, 下载37次)