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Essential C# 2.0 gives you just what you need to quickly get up and running writing C# applications

关于优化的svm寻优算法，是用ga算法优化的，过几天我会上传基于布谷鸟算法的优化算法(About optimization svm optimization algorithm is ga algorithm optimization, a few days I will upload cuckoo algorithm based optimization algorithm)

频率估计的ESPRIT方法,实现频率的精确估计，并可推广到空间到达角的估计(frequency estimation with ESPRIT method)

说明：  PSO源程序，可以任意优化两维数据点和多维数据点，在优化多维数据时，要将初始矩阵变成多维矩阵(PSO source code, you can optimize an arbitrary two-dimensional data point and multi-dimensional data points, in the optimization of multi-dimensional data, to the initial matrix into a multi-dimensional matrix of)

说明：  两种不同的假设： H1 ： 0 xn A fn wn ( ) cos(2 ) ( ) = ++ π θ n=1,2,…,N，f0 为规一化频率 H0 ： xn wn () () = n=1,2,…,N 其中 w[n]是均值为 0，方差为 2 σ n 的高斯白噪声，A 已知，样本间相互 独立，信号与噪声相互独立; 相位θ 是随机变量，它服从均匀分布 1 0 2 ( ) 20 p θ π θ π ?? ≤ ≤ = ??? 其它 1）改变输入信噪比（改变 A 或噪声方差均可），给定虚警概率，画出 输入信噪比与检测概率之间的理论曲线。(注意：理论检测曲线与样本 数有关) 2）改变样本数，用 Monte-Carlo 实验方法得出 PF＝0.001 时输入信噪比 与检测概率之间关系曲线（至少三条），并得出结论。 3）改变 M-C 实验次数，样本数不变，用 Monte-Carlo 实验方法得出 PF ＝0.001 时输入信噪比与检测概率之间关系曲线（至少三条），并得出 结论。(Draw the theoretical curve between input SNR and detection probability)

压缩文件中有两个文件，其中一个是基于BP、RBF人工神经网络解决变压器故障诊断的问题。另外一个文件是用进退法、黄金分割法、DFP、Powell的方法解决函数最小值。( There are two archive files, one of which is based BP, RBF artificial neural network to solve the problem of transformer fault diagnosis. Another document is the solution to function with the minimum of advance and retreat method, golden section method, DFP, Powell method.)

passive Anti-islanding method(PJD)

intelligent control notes

[model] = SVMTRAIN(X, Y, C, kernelFunction, tol, max_passes) trains an SVM classifier and returns trained model. X is the matrix of training examples. Each row is a training example, and the jth column holds the jth feature. Y is a column matrix containing 1 for positive examples and 0 for negative examples. C is the standard SVM regularization parameter. tol is a tolerance value used for determining equality of floating point numbers. max_passes controls the number of iterations over the dataset (without changes to alpha) before the algorithm quits.

Matlab遗传算法工具箱源码,添加路径后可直接调用遗传算法函数(Matlab GA Tools source code)