Code-Generation-

几篇关于小波的应用和相关matlab程序(Several applications of wavelets and related matlab program)

网上找来的共享的资源，结合matlab和C对图像进行灰度形态学的变换，为进一步的处理做准备。(Online recruited to shared resources, combined with matlab and C of the image gray-scale morphology of the transformation, in preparation for further processing.)

autocorrelation matlab file

Tdm ver10 is a code in LabView for TDM communications

采用贝叶斯正则化算法提高 BP 网络的推广能力,采用两种训练方法(Bayesian regularization algorithm to improve the generalization ability of BP network, using two training methods)

说明：  按FPE定阶的 源程序：fpe.cpp M序列：M序列.txt 白噪声：Gauss.txt 程序中先用依模型阶次递推算法估计模型的参数，再用fpe方法判断模型的阶次。 程序运行结果如下： n: 1 判断阶次FPE的值: 0.0096406 -0.481665 1.07868 n: 2 判断阶次FPE的值: 0.00875755 -0.446739 0.00498181 1.07791 0.0527289 n: 3 判断阶次FPE的值: 0.0087098 -0.459433 0.120972 -0.0569228 1.07814 0.0390757 0.116982 n: 4 判断阶次FPE的值: 0.000396884 -0.509677 0.4501 -0.200906 0.0656188 1.07991 -0.0156362 0.442989 0.0497236 n: 5 判断阶次FPE的值: 3.2095e-007 -1.18415 0.813123 -0.517862 0.34881 -0.116864 1.07999 -0.744141 0.474462 -0.253112 0.122771 n: 6 判断阶次FPE的值: 3.23349e-007 -1.14659 0.76933 -0.487651 0.329676 -0.10377 -0.00440907 1.07999 -0.703574 0.447253 -0.235282 0.113587 0.00479688 从以上结果可以看出，当n=5时，fpe值最小，所以这时的模型阶次和参数估计值为最优结果： 3.2095e-007 -1.18415 0.813123 -0.517862 0.34881 -0.116864 1.07999 -0.744141 0.474462 -0.253112 0.122771(err)

针对模型中人为设定参数值的不合理性，对机动时间常数的倒数进行建模，提出新的模型，并给出相应的α-Jerk滤波算法。该模型在估计目标状态的同时，能够实时估计参数α的值。仿真结果表明α-Jerk模型比Jerk模型收敛速度更快，状态估计更精确。并与jerk算法进行了比较。(Artificially set for the model parameter values ​ ​ of the irrational nature of the reciprocal of the motor time constant modeling, and propose new models and the corresponding alpha-on Jerk filtering algorithm. The model to estimate the target state at the same time, real-time estimate of the value of the parameter α. The simulation results show that alpha-on Jerk model faster convergence than the Jerk model, state estimation more accurate. And jerk algorithm.)

Matlab Codes for Series Resenance

matlab中一些基本的gui界面设计及操作(Some basic matlab gui interface design and operation)

在matlab7.4基础上实现恒虚警检测,包括单元平均CFAR检测实例过程，杂波边缘虚警性能，GO-CFAR等(Achieve CFAR in matlab7.4 basis, including cell averaging CFAR detection instance procedure, clutter edge false alarm performance, GO-CFAR, etc.)