PCA

This mex-file reads SHPT_POINT, SHPT_MULTIPOINT, SHPT_ARC, and SHPT_POLYGON shapefiles into structures. Other requirements： Requires shapelib (http://shapelib.maptools.org). (This mex-file reads SHPT_POINT, SHPT_MULTIPOINT, SHPT_ARC, and SHPT_POLYGON shapefiles into structures. Other requirements： Requires shapelib (http://shapelib.maptools.org).)

说明：  实现无线传感器网络或者蜂窝网中的三种节点定位算法：CLS、质心算法、DV-Hop算法(Wireless sensor networks or cellular networks of the three localization algorithms: CLS, centroid algorithm, DV-Hop Algorithm)

SOFTWARE DEFECT ASSOCIATION MINING

说明：  matlab gui例子 对初学者会有较大帮助(matlab gui)

运用MATLAB编程并结合simulink解决简单的电路问题。 自选题目：图1所示为典型的直流电阻电路[3], 含有电压控制的受控电流源VCCS，其中，R1=1Ω,R2= 2Ω, R3=3Ω,US=10V, IS=15A, VCCS= ，现需分析计算电流i1和电压u2 (Combined with the use of MATLAB simulink program to solve simple circuit problems. Optional Title: Figure 1 shows a typical DC resistance of the circuit [3], with voltage-controlled current source controlled VCCS, which, R1 = 1Ω, R2 = 2Ω, R3 = 3Ω, US = 10V, IS = 15A, VCCS =, now need to be analyzed to compute the current and voltage u2 i1)

说明：  空间杆有限元分析，包中有详细的WORD文档说明(Space bar finite element analysis, a detailed package documentation WORD)

差分进行算法程序，一共是6个，包括编码，交叉，变异等程序(Difference to algorithm program, a total of six, including coding, crossover and mutation procedures)

Series and Prallel Feedbaack

MATLAB上实现的OFDM+AWGN/Rayleigh+BPSK/QPSK/16QAM/64QAM，并分析EbN0 vs BER.(The MATLAB implementation OFDM+AWGN/Rayleigh+BPSK/QPSK/16QAM/64QAM, and analyze EbN0 vs BER.)

The optimization behavior of the self-adaptation (SA) evolution strategy (ES) with intermediate multirecombination (the (=I )-SA-ES) using isotropic mutations is investigated on the general elliptic objective function. An asymptotically exact quadratic progress rate formula is derived. This is used to model the dynamical ES system by a set of difference equations. The solutions of this system are used to analytically calculate the optimal learning parameter  . The theoretical results are compared and validated by comparison with real (=I )-SA-ES runs on typical elliptic test model cases. The theoretical results clearly indicate that using a model-independent learning parameter  leads to suboptimal performance of the (=I )-SA-ES on objective functions with changing local condition numbers as often encountered in practical problems with complex fitness landscapes.