LMS-filter

Kalman Filtering and Neural Networks

for high precision wind energy conversion sysytems this fuzzy controller can be used

Jacobi算法和共轭梯度法的基本matlab程序，rar压缩包，包括2个文件(Jacobi conjugate gradient algorithm and the basic matlab program, rar archive, including the two documents)

FCM algorithm optimisation by genetic algorithm

matlab 可视化 微分方程 物理 算法 超越方程 科学计算 数学变换 最值问题 图像增强 图形界面 线性方程 元胞自动机 晶体生长 金融(physical algorithm matlab visualization in scientific computing differential equations of mathematical transformations beyond the most value problem of linear equations graphical image enhancement of cellular automata Crystal Growth Finance)

说明：  AUC calculation, including mapping, calculation value

GPC算法实现 自校正控制 智能预测控制(GPC adaptive control algorithm intelligent predictive control)

一个处理化学数据的MATLAB程序，用于处理产生畸变的数据的二次拟合(A chemical treatment data MATLAB procedures for dealing with distortion of data arising from the second fitting)

说明：  输电线路勘测设计的matlab的一个有用的源码(transmission line survey and design of Matlab a useful source)

将压缩感知用于谱估计中，根据论文谱压缩感知的一些程序(Compressive sensing (CS) is a new approach to simultaneous sensing and compression of sparse and compressible signals based on randomized dimensionality reduction. To recover a signal from its compressive measurements, standard CS algorithms seek the sparsest signal in some discrete basis or frame that agrees with the measurements. A great many applications feature smooth or modulated signals that are frequency sparse and can be modeled as a superposition of a small number of sinusoids. Unfortunately, such signals are only sparse in the discrete Fourier transform (DFT) domain when the sinusoid frequencies live precisely at the center of the DFT bins. When this is not the case, CS recovery performance degrades significantly. In this paper, we introduce a suite of spectral CS (SCS) recovery algorithms for arbitrary frequency sparse signals. The key ingredients are an over-sampled DFT frame, a signal model that inhibits closely spaced sinusoids, and classical sinusoid parameter e)