additivePnoisePchannel

PANELS开发基于DOS环境下用户界面... 软件类别：窗口界面 运行环境：Win9x/NT/2000/XP/ (PANELS development environment based on DOS user interface ... software categories : window interface operating environment : Win9x/NT/2000/XP /)

讲述利用MATLAB实现特殊矩阵，以及实现MATLAB的计算结果的图形化( About the use of MATLAB to achieve a special matrix, as well as the calculation of the results of MATLAB graphical )

说明：  实现大容量的图像信息隐藏，采用双随机置乱算法(Realize large-capacity image information hiding, using double-random scrambling algorithm)

时频分析，用于时频分析，压缩文件，概率密度曲线 ，计算样本向量x的概率密度估计，返回在xi点的概率密度f，此时我们使用plot(xi,f)就可以绘制出概率密度曲线。(Time-frequency analysis, time-frequency analysis is used to compressed files)

关于MATLAB方面的教程主要是关于如何应用等基本操作(About MATLAB aspects tutorial is about how to apply the basic operations)

ests for KeyGenerator constructor and methods.

rbf神经网络软测量matlab程序，svm软测量matlab程序(RBF neural network soft measurement Matlab procedures, SVM soft Matlab procedures)

插值与拟合的matlab实现，计算两个矩阵之间的欧氏距离，这个有中文注释，看得明白。( Interpolation and fitting matlab implementation, Calculation of the Euclidean distance between the two matrices, The Chinese have a comment, understand it.)

本程序以matlab为基础针对飞行目标的预测来建模的编程(This procedure to matlab-based objectives for the flight to prediction modeling programming)

在机动目标跟踪中，机动目标模型是机动目标跟踪的基本要素之一，一般希望机动目标模型能准确表征目标机动时的各种运动状态。比较常用的模型有匀速运动(CV)模型、匀加速运动(CA) 模型、时间相关模型（Singer）和机动目标“当前”统计模型。上述模型均采用机动频率表征目标的机动情况。在应用当中，通常采用固定的机动频率，这就表示机动目标的机动时间是一定的，而实际上机动目标的机动时间是不断变化的，也就是说机动频率是不断变化的，采用固定机动频率必然会带来误差。采样周期在0.5—2S时，机动频率越小跟踪精度越高[1]，但机动频率仍然是固定值。本文提出的基于神经网络的机动频率自适应调整方法可以使机动频率随机动而变化，从而提高状态估计的准确性，提高跟踪精度。本文将小波神经网络用于机动目标跟踪中机动频率的自适应调整，该算法对机动目标“当前”统计模型中的机动频率进行实时修改, 从而自适应的改变机动频率,使跟踪算法与目标的真实状态更接近。该算法采用小波神经网络的离线训练，实时性好。(The maneuver of the maneuvering target is uncertain. The maneuvering frequency is constantly changeable, but traditionally it is beforehand determined as a constant based on the target state estimation in the state model of the maneuvering target. The maneuver of the maneuvering target makes the kinematics equation of the target model mismatch with the practical motion model and the tracking error will be increased. Based on the advantages of the self-learning, the rapid convergence rate and the nonlinear approximation ability of the wavelet neural network, it was put forward to be used in the field of target tracking in the paper. The new residual is used as the input of the wavelet neural network, the output of the network is used to adjust adaptively the maneuvering frequency of the CS model. The algorithm is more close to the real state of the target. The simulation results showed that tracking error can be reduced and the tracking accuracy can be improved.)