leach-m(1)

说明：  给出了分形计算的源代码的matlab程序，可以迅速帮助大家进行分形的分析与计算，参数容易设置(given fractal calculation of the source code Matlab procedures, we can quickly help fractal analysis and calculation of parameters easy to set up)

自适应的最小均方算法：只要自适应线性组合器每次迭代运算时都知道输入信号和参考响应，选用LMS算法很合适。(Adaptive least mean square (LMS) algorithm: as long as the adaptive linear combiner for each iteration when they know that the input signal and reference response, selection of LMS algorithm is very appropriate.)

k-means on matlab .........

说明：  EKF仅仅利用了非线性函数Taylor展开式的一阶偏导部分（忽略高阶项），常常导致在状态的后验分布的估计上产生较大的误差，影响滤波算法的性能，从而影响整个跟踪系统的性能。最近，在自适应滤波领域又出现了新的算法——无味变换Kalman滤波器（Unscented Kalman Filter-UKF）。UKF的思想不同于EKF滤波，它通过设计少量的σ点，由σ点经由非线性函数的传播，计算出随机向量一、二阶统计特性的传播。因此它比EKF滤波能更好地迫近状态方程的非线性特性，从而比EKF滤波具有更高的估计精度。 (EKF only uses non-linear function of the first-order Taylor expansion of some partial derivatives (ignoring higher order terms), often leading to the posterior distribution of the state estimates to generate large errors affect the performance of filtering algorithms, which affect the whole tracking system performance. Recently, the field of adaptive filtering algorithms and the emergence of new- and tasteless transform Kalman filter (Unscented Kalman Filter-UKF). EKF UKF filter is different from the idea that it points through the design of a small amount of σ by σ point spread through the nonlinear function to calculate the random vector first and second order statistical properties of the transmission. Therefore it is better than the EKF filter nonlinear characteristics equation of state approach, which is more than the EKF filter estimation accuracy.)

包含了一个蚂蚁算法中的蚁密系统，程序运行良好！(Contains a dense ant ant algorithm in the system, run good!)

只要认真看， 一天就可以掌握DFT的原理，大家看看哦(As long as a day, you can master the principle of the DFT, we look at)

采用二进制编码，精度高，解决多目标优化问题。可根据个人需要进行改进。(Nice genetic algorithm procedures)

基于BOOST斩波电路的MTALAB仿真，仿真结果正确，开关电源设计必备仿真(BOOST chopper MTALAB based simulation, simulation results are correct, switching power supply design must Simulation)

Notch filter demonstration

的基本函数介绍符号数据、内联函数、unit 数组、稀疏类）(Basic functions introduced symbolic data, inline functions, unit array, sparse class))