遗传算法学习资料

遗传基因算法源代码(资料)，C++源代码，是研究遗传算法的入门程序。-genetic algorithm source code (information), C source code, is to study the genetic algorithm entry procedures.

基于神经网络的语音识别技术研究 基于神经网络的语音识别技术研究-Neural Network Based Speech Recognition Technology based on neural network speech recognition technology research

一个采用VC编写的神经网络源代码，供大家参考。-VC prepared using a neural network source code for reference.

很好的matlab模式识别工具箱，帮助大家建立典型的模式识别模式。-Good pattern recognition matlab toolbox to help the U.S. establish a typical pattern recognition model.

用Delphi语言编写的遗传算法，内容较多，可以参考-Delphi language prepared by the genetic algorithm, a great deal of content, they can refer to

人工神经网络网上发给我的BP-ANN Internet to my BP

基于神经网络的文字识别系统,它通过遗传算法实现。-Neural Network Based Character Recognition system, which through genetic algorithm.

nnToolKit 神经网络工具包是基于 MATLAB 神经网络工具箱自行开发的一组神经网络算法函数库，其中的函数在MATLAB环境下均可独立运行，并可打包成DLL组件，这些组件作为独立的COM对象，可以直接被Visual Basic、Visual C++ 、C++ Builder或其它支持COM的高级语言所引用。本工具包中包含的算法包括BP算法中的一些改进算法、模糊神经网络、小波神经网络、遗传算法优化神经网络权值改进算法等，根据需要，读者还可以对该工具包进行扩展，即扩充一些新的函数，以实现特殊的算法需求。-nnToolKit neural network tool kit is based on the MATLAB neural network toolbox developed by a group of neural network algorithm library, which functions in the MATLAB environment can be run independently, and packaged into a DLL components, these components as a separate COM object, can be directly by Visual Basic, Visual C++, C++ Builder or other high-level language support COM quoted. The toolkit includes algorithms, including some of the BP algorithm to improve the algorithm, fuzzy neural network, wavelet neural networks, genetic algorithms to optimize neural network weights to improve the algorithm, if necessary, readers can also expand the tool kit, namely the expansion of some new function, the algorithm in order to achieve sp

数据挖掘中常常会遇到apriori算法 这是apriori算法的java版-data mining algorithm will often encounter this algorithm is algorithm java version of the algorithm

围绕粒子群的当前质心对粒子群重新初始化.这样，每个粒子在随后的迭代中将在新的位置带着粒子在上次搜索中获得的“运动惯性”（wvi）向Pi，Pg的方向前进，从而可以在粒子群的运动过程中获得新的位置，增加求得更优解的机会.随着迭代的继续，经过变异的粒子群又将趋向于同一点，当粒子群收敛到一定程度时又进行下一次变异，如此反复，直到迭代结束.-particle swarm around the center of mass of the current PSO reinitialization. Thus, Each particle in the next iteration will be in the new location with particles in the last search was the "inertia" (wvi ) Pi, Pg orientation, and thus can PSO course of the campaign was a new position, increase seek better solutions opportunities. With the continued iteration, after variation of PSO will tend to the same point. When PSO converge to a certain extent when the next variation, so repeatedly, until the end of iteration.