用matlab编的，一个典型的BP神经网络的例子，相信对你的学习、工作都能起到一个参考的作用...

Using Simulated annealing to solve TSP

用于求解多目标优化问题，多目标进化算法源程序-The code for multi-objective optimization algorithm

神经网络中自适应谐振理论模型实现的C语言源程序！-Neural network adaptive resonance theory model of the C language source code!

利用ICA变换进行人脸检测,是一种新的人脸检测方法-ICA transform the use of face detection, is a new face detection method

Schapire 所著的Boosting算法的教程。 对目前物体识别和机器学习领域中广泛应用的Boosting算法展开了深入浅出的描述，还有很多Toy Examples。-Tutorial for Boosting algorithm by Schapire

人工神经网络算法研究及应用 田景文.包括神经网络、遗传算法、小波网络、模拟退火算法和支持向量机，清晰易懂，非常值得一看。-Artificial Neural Network Algorithm Research and Application田景文. Including neural networks, genetic algorithms, wavelet networks, simulated annealing algorithm and support vector machine, clear and easy to understand, is a must see.

本工具包主要是为对神经网络有兴趣人士提供的一种方便，灵活的学习和研究软件。 JNNT由java语言写成，具有跨平台的优越性能.java applet的演示版更简单到只需要任何机器上的浏览器就可以运行，无需安装任何大型附加软件。更方便爱好者通过internet远程访问资源。 支持反向传播算法(BP),LBG聚类法和径向基网络(RBF) -This toolkit is for people interested in neural networks has provided a convenient, flexible learning and research software. JNNT by the java language, cross-platform with superior performance. Java applet demo version is more simple to just any machine can run a browser, without installing any large-scale add-on software. Enthusiasts through the internet more convenient remote access to resources. To support the back-propagation algorithm (BP), LBG clustering method and radial basis function network (RBF)

人工智能AI经典入门书，很难找的噢，给喜欢的朋友-classical AI Artificial Intelligence portal, it is very difficult to find the, uh, likes to friends

基与多种智能方法和模式识别技术的研究,如果你也在从事相关研究,一定要看看,我想应该有借鉴价值-base with a variety of intelligent methods and pattern recognition techniques, but if you are engaged in related research, we must look at, I think there should be a reference value

C＋＋编写的机器学习算法 Lemga is a C++ package which consists of classes for several learning models and generic algorithms for optimizing (training) the models-prepared by the machine learning algorithm Lemga C is a package which con sists of classes for several learning models an d generic algorithms for optimizing (training ) the models