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matlab实现具有混沌特性的Lorenz系统的二维三维图形(matlab implementation of the Lorenz chaotic system with two-dimensional three-dimensional graphics)

说明：  一种最简本的基于最小均方误差的自适应算法(One of the most simple minimum mean square error of the adaptive algorithm)

在振动台试验后由测得的加速度值两次积分求位移(Acceleration of displacement)

Hard decision spectrum sensing using neural network.

基本电路，用于实现低通滤波器，可以仿真波形。(failed to translate)

z源逆变器简单升压控制的另一种改进的算法及仿真例程，只需改变占空比即可实现直流侧电压的改变，相对于传统的算法简单，更容易理解。(Z source inverter simple boost control of another kind of improved algorithm and simulation routines, only need to change the duty cycle of dc side voltage change can be realized, compared with the traditional method is simple, easy to understand. )

说明：  使用matlab语言实现的时域有限差分算法(finite differentiate time domain)

形态学处理 包括开运算，闭运算，边缘检测，重构等　matlab源码(Morphological opening treatment, including operations, closing operation, edge detection, reconfiguration, such as source code matlab)

This paper presents a new approach to image segmentation using Pillar K-means algorithm. This segmentation method includes a new mechanism for grouping the elements of high resolution images in order to improve accuracy and reduce the computation time. The system uses K-means for image segmentation optimized by the algorithm after Pillar. The Pillar algorithm considers the placement of pillars should be located as far from each other to resist the pressure distribution of a roof, as same as the number of centroids between the data distribution. This algorithm is able to optimize the K-means clustering for image segmentation in the aspects of accuracy and computation time. This algorithm distributes all initial centroids according to the maximum cumulative distance metric. This paper evaluates the proposed approach for image segmentation by comparing with K-means clustering algorithm and Gaussian mixture model and the participation of RGB, HSV, HSL and CIELAB color spaces.

对粒子滤波实现的很好，很实用的matlab实现程序，望广大学习者放心使用。(Particle filter to achieve good, very practical realization of the matlab program, hope the majority of learners at ease.)