一个vc开发的支持向量机程序，对于初学者很有用．共享以下．...

随机神经网络的源代码，对于研究神经网络的人来说，可能有一定的参考作用-random neural network source code for research neural network people, may have some reference

hmm算法 包括前后向算法、viterbi算法、bw算法等另外包括内存申请等源代码-algorithm to include before and after algorithm, Viterbi algorithm, bw algorithm other memory applications including source code

hough变换的代码，这个变换可以原来检测直线或圆等图形。-Hough Transform code, can transform the original circular or linear detection, such as graphics.

bp反向传播算法，用c语言编写，用于航空运输预测。

Autolife模型是一个能够进行“开放式进化”的人工生命系统。每个Agent模型采用可以变化规则表长度的有限自动机模型建模。一方面Agent可以进行自我繁殖，同时模型中的选择机制没有采用显式的适应度函数而是采用能量消耗的简单模型而自发涌现出来，所以可以认为Agent模型是一个类Tierra系统。然而与Tierra、Avida等数字生命模型不同的是，Autolife模型进行了大大的简化，它界面友好，操作直接。虽然没有给每个Agent装配一个虚拟计算机，但是Agent与环境的耦合则可以看成一个图灵机模型，因此Agent可以通过变异而“任意”的编程序。通过Autolife模型，首先人们可以看到一般的生态系统中共存的现象：生物的大爆炸、大灭绝，Agent进化得越来越聪明；其次，用户可以通过变化不同的食物添加规则探索Agent与环境的关系；最后，如果允许Agent通过播种改变环境自动产生食物，那么组织的涌现就是一种不可避免的结果。Agent构成的组织具有自主运动的特性，还可以进行自我修复，可以说Autolife中的组织是一些真正的“活体”。-Autolife model is to conduct an "open-evolution" artificial life system. Each Agent can be used model changes the rules limited the length of the table automatic machine modeling. One Agent can be self-propagation, model selection mechanism not used Explicit the fitness function instead of using the energy consumption of a simple model to emerge spontaneously, so that the agent can be a model category Tierra system. But with Tierra, Avida digital life model is different Autolife model for greatly simplified its

基于遗传算法的图像模式识别 ,这个是本人收藏的有关遗传算法的源程序-based on genetic algorithm for image pattern recognition, this is my collection of the genetic algorithm source code

内附两个源代码，其中一个源代码是识别程序的，另一个是一个矩阵类库的，所以一并附上。 ~..~因为已经保存了训练好的网络权值，所以第一次使用识别程序时，可以直接运行Release目录下的可执行文件，然后对图片目录中的测试图片进行读入、识别。 当然读者也可以自行用训练样本训练网络，不过要特别注意训练样本的选择，否则可能造成识别率很低。训练样本选择的原则是，尽可能的有代表性，在训练时间不至于太长的情况下，训练样本数目尽量多。 -containing 2 source code, which is a source identification procedures, and the other is a matrix class library. So accompanied. ~ ~ .. Has been preserved because of the trained network weights, the first use of the identification procedures, can be run directly Release directory executable file, Then the catalog of tests conducted read into the picture, recognition. Certainly readers can use their own training samples training network, but special attention should be paid to the choice of training samples, Otherwise, it could cause a very low recognition rate. Training sample selection principle is that as far as possible be representative, in the training of the time not too long under the circumstances, the number of training samples as much as possible.

OPENCV系列的，关于CORE，应该挺有用的，大家自己看看吧！-OPENCV series of CORE, should be quite useless, we all see!

全面讲解了有视觉机器人的解决方案，强烈推荐-comprehensive account of a robot visual solutions and strongly recommended

遗传算法和神经网络结合，利用两种算法的优点，有很好的参考价值-Genetic algorithms and neural networks combine the advantages of using two algorithms, a very good reference value