GABP

按照给定指标采用模拟低通滤波器（巴特沃思低通）去逼近理想模拟低通滤波器 ，然后通过双线性变换法对模拟滤波器进行数字化，得到数字滤波器 。(Indicators in accordance with a given analog low-pass filter (Butterworth low-pass) to approximate the ideal analog low-pass filter, and then through the bilinear transformation method of digitizing analog filters, the digital filter.)

基于广义动态模糊神经网络的船舶纵摇参考程序(Shake reference procedures based on generalized dynamic fuzzy neural network trim of the ship)

光缆线布建标准,布建四大要素,光纤的设计,光纤采购与安全注意事项(A standard optical fiber cable distribution, deployment four elements, the design of optical fiber, optical fiber procurement and safety precautions)

DCT域数字水印嵌入算法MATLAB实现(DCT domain watermarking algorithm MATLAB Implementation)

整数小波变换与图像压缩，逆变换源码，实现行、列逆变换，奇偶行列逆重排(Integer wavelet transform and image compression, inverse transform source code to achieve row, column inverse transform, rearrangement of odd and even ranks of the inverse)

matlab code for genetic algorithm

这里用模糊函数对Barke进行分析，研究其时间和频率的 分辨率。(barker )

这是关于将卡尔曼于压缩感知结合的的matlab程序。(This is about the combination of the compressed sensing Kalman matlab program.)

用于检验两个时间序列是否具有格兰杰因果关系(Used to test whether two time series have Granger causality)

相关向量机(RVM)的matlab源程序，包含快速算法，内含代码使用说明。 RVM采取是与支持向量机相同的函数形式稀疏概率模型，对未知函数进行预测或分类。 优点： (1) 不仅仅输出预测目标量的点估计值,还可以输出预测值的分布. (2) 使用更少数量的支持向量,从而显著减少输出目标量预测值的计算时间. (3) RVM不需要估计过多的参数. (4) RVM对是否满足Mercer 定理的核函数没有限制,适应性更好(Relevance Vector Machine (RVM) of the matlab source code, including fast algorithm that contains code for use. RVM support vector machine is taken the same functional form sparse probabilistic model to predict the unknown function or classification. Advantages: (1) is not only the amount of output predicted target point estimates, but also the distribution of predicted values ​ ​ can be output. (2) using a smaller number of support vectors, thereby significantly reducing the output target amount predicted value calculation time. (3) RVM does not require excessive parameter estimation. (4) RVM meets Mercer theorem on the kernel function is not limited, and better adaptability)