7-chapter

uses partial correlation function and filter to implement an AR model

实现聚类K均值算法： K均值算法：给定类的个数K，将n个对象分到K个类中去，使得类内对象之间的相似性最大，而类之间的相似性最小。 缺点：产生类的大小相差不会很大，对于脏数据很敏感。 改进的算法：k—medoids 方法。这儿选取一个对象叫做mediod来代替上面的中心 的作用，这样的一个medoid就标识了这个类。步骤： 1，任意选取K个对象作为medoids（O1,O2,…Oi…Ok）。 以下是循环的： 2，将余下的对象分到各个类中去（根据与medoid最相近的原则）； 3，对于每个类（Oi）中，顺序选取一个Or，计算用Or代替Oi后的消耗—E（Or）。选择E最小的那个Or来代替Oi。这样K个medoids就改变了，下面就再转到2。 4，这样循环直到K个medoids固定下来。 这种算法对于脏数据和异常数据不敏感，但计算量显然要比K均值要大，一般只适合小数据量。(achieving K-mean clustering algorithms : K-means algorithm : given the number of Class K, n will be assigned to target K to 000 category, making target category of the similarity between the largest category of the similarity between the smallest. Disadvantages : class size have no great difference for dirty data is very sensitive. Improved algorithms : k-medoids methods. Here a selection of objects called mediod to replace the center of the above, the logo on a medoid this category. Steps : 1, arbitrary selection of objects as K medoids (O1, O2, Ok ... ... Oi). Following is a cycle : 2, the remaining targets assigned to each category (in accordance with the closest medoid principle); 3, for each category (Oi), the order of selection of a Or, calculated Oi Or replace the consumption-E (Or))

在matlab的基础算法上，以zigbee的小蜜蜂最小路径代码用c语言或C++语言转变来的代码(matlab suanfa zigbee)

说明：  单相逆变器simulink仿真，用MATLAB的Simulink模块对单相逆变器进行仿真，并绘制波形。(Simulink simulation of single-phase inverter, use Simulink module of MATLAB to simulate single-phase inverter and draw waveform.)

Theory and Applications of OFDM and CDMA Wideband Wireless Communications Henrik Schulze and Christian L¨uders Both of Fachhochschule S¨udwestfalen Meschede, Germany

连续搅拌反应釜CSTR系统是聚合反应工业中广泛使用的重要系统，它是一个很强的非线性系统，其系统建模、状态估计和实时控制等问题，(Continuous stirred tank reactor CSTR polymerization system is widely used in industry-critical systems, it is a very strong non-linear system, the system modeling, state estimation and real-time control problems,)

this is file .m that can be run at matlab

L2N2 Automata in Cellular Automata Matlab Code

AVERAGE FILTER IN IMAGE PROCESSING

In this paper a Conventional PD Controller and Fuzzy Neural Controller for trajectory tracking of mobile robot platform is presented. Using computed torque method is given input- torque on mobile robot wheels. The locomotion control structure is based on integration of kinematics and dynamics model of mobile robot base. The proposed Control scheme and Fuzzy Neural Algorithm could be useful for building an autonomous non-destructive testing system based on wheeled mobile robot. Fuzzy Neural Network-Rule base, is optimized using Genetic Algorithm. Simulations are done using Matlab software. Key words:Mobile Robot, Conventional, Fuzzy Logic, Neural Network, Genetic Algorithm, trajectory tracking,