PSO-GRNN

krige插值，主要用于地统计学的科学计算，从国外网站找到。(interpolation krige ，kriging，cokriging)

说明：  根据turbo编码器的生成多项式对输入信息进行编码(turbo encoder)

提出利用电云进行模型重构的算法，该算法基于点云的几何形状特征，很好的算法。(Proposed the use of electric cloud model reconstruction algorithm based on the geometric shape of the point cloud, a good algorithm.)

CPM调制的线性均衡器，使用维特比判决器进行判决。(CPM modulation linear equalizer, the use of Viterbi browser sentencing verdict.)

IS-95A Reverse Traffic Channel Open and Closed Loop Power Control complete simulink simulation

说明：  a document for fuzzy classification

说明：  ADMM是一个高效的优化算法，可以用在很多领域中，1范数是一种常用的目标函数。(ADMM is an effective, fast optimizing algorithm and can be used in many applications. 1-norm is a commonly used objective function.)

说明：  比较三次样条插值和拉格朗日插值多项式对runge函数插值的效果并作图解释(Comparison of cubic spline interpolation and Lagrange interpolation polynomial interpolation of the Runge function and mapping to explain the effect of)

matlab gui code for hiding image

独立成份分析（ＩＣＡ）以及ｗｉｎｎｅｒ滤波 Source separation of complex signals with JADE. Jade performs `Source Separation in the following sense: X is an n x T data matrix assumed modelled as X = A S + N where o A is an unknown n x m matrix with full rank. o S is a m x T data matrix (source signals) with the properties a) for each t, the components of S(:,t) are statistically independent b) for each p, the S(p,:) is the realization of a zero-mean `source signal . c) At most one of these processes has a vanishing 4th-order cumulant. o N is a n x T matrix. It is a realization of a spatially white Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance sigma. This is probably better than no modeling at all...( Source separation of complex signals with JADE. Jade performs `Source Separation in the following sense: X is an n x T data matrix assumed modelled as X = A S+ N where o A is an unknown n x m matrix with full rank. o S is a m x T data matrix (source signals) with the properties a) for each t, the components of S(:,t) are statistically independent b) for each p, the S(p,:) is the realization of a zero-mean `source signal . c) At most one of these processes has a vanishing 4th-order cumulant. o N is a n x T matrix. It is a realization of a spatially white Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance sigma. This is probably better than no modeling at all...)