panel

使用matlab的simulink工具箱，对平面二连杆机构进行动态仿真(Matlab dynamic simulation of Two-linkage mechanism)

这是一道很基本的程序，一方面希望它对初学遗传算法的兄弟们有用（包含了遗传算法实现的大部分步骤，而且有详细的注释），另一方面旨在抛砖引玉， 在下浅见： 1、遗传算法在进行大规模计算时，数据交换量大，速度确实是一个瓶颈，就如这道程序，在种群规模50，进化代数1000时运行需要40秒左右(cpu:duron 1G),当然，小弟编程能力弱，望各位大侠指教啊，或帮我修改一下这道程序 (This is a very basic procedure, While they want it beginner GA brothers useful (including the genetic algorithm to achieve the most steps and a detailed note), on the other hand aims to attract the next suggestions : 1. GA conducting large-scale calculations, the data exchange capacity, speed is a bottleneck, as this practice. 50 in population size, the evolution of algebra required for the operation of 1000 when about 40 seconds (cpu : duron 1G), Of course, the younger programming weak, and hopes to enlighten ah Shanhaiguan, or help me rephrase this practice)

Parameter+Estimation+and+Inverse+Problems 该书的配套光配内容(Parameter+ Estimation+ and+ Inverse+ Problems with the contents of the book' s supporting light)

基于马尔科夫链概念，5*5矩阵的概率转移矩的matlab实现(Based on the concept of Markov chain, the realization of the 5* 5 matrix s transfer probability matrix in matlab)

本程序实现了找出一个n个数的序列中最长的单调递增子序列,并符带测试数据(This program implements to find a sequence of n the number of the longest monotonically increasing subsequence, and to break with the test data)

upml边界条件下的FDTD程序，感觉很不错，分享给大家吧，也希望大家能更多的分享自己的程序。谢谢(boundary conditions under upml FDTD program, I feel very good, for everyone to share it, I hope you will be able to share their program more. Thank you)

东南大学MATLAB课件，第一章 MATLAB概述，第二章 MATLAB数值运算，第三章 MATLAB符号运算……(Southeast University, MATLAB courseware, chapter MATLAB overview of Chapter MATLAB numerical computing, symbolic computing MATLAB Chapter ... ...)

matlab仿真双级矩阵变换器的逆变程序，包括判断扇区，计算相位角等(matlab simulation of two-stage matrix converter inverter procedures, including the judge sectors, calculating the phase angle)

In the distributed processing, where common labeled data may be not available for designing classifier ensemble, however, an ensemble solution is necessary, traditional fixed decision aggregation could not account for class prior mismatch or classifier dependencies in electronic technology. Previous transductive learning strategies have several drawbacks, e.g., feasibility of the constraints was not guaranteed and heuristic learning was applied. We overcome these problems by developing improved iterative scaling (IIS) algorithm for optimal solution. This method is shown to achieve improved decision accuracy over the earlier approaches in electronic technology

GMDH Neural Network Implementation