遗传算法的示例

一个聚类算法，这是清华大学电子系OCR实验室的人写的-a clustering algorithm, which is the Department of Electronics, Tsinghua University OCR laboratory wrote in the

本程序能实现专家PID控制。专家控制的实质是基于受控对象和控制规律的各种知识，并以智能的方式利用这些知识来设计控制器。-the procedures to achieve expert PID control. Experts control of the controlled substance is based on the object and control the various law knowledge, and the intelligent use of this knowledge to design controller.

对于含有噪声的的数字图像模式“1、2、3、4”，使用“hopfield”神经网络，进行联想、识别，以去除噪声的影响。-for containing noise of the digital image model "1,2,3,4", "hopfield" neural network, Lenovo, identification, in order to remove noise impact.

Using Simulated annealing to solve TSP

bp神经网络的工具包-bp neural network tool kit

用C++实现了期望最大化算法，为了简化，矩阵使用对角矩阵-C achieve expectations maximize algorithm, in order to simplify the use of matrix diagonal matrix

　人工神经网络(Aartificial Neural Network，下简称ANN)是模拟生物神经元的结构而提出的一种信息处理方法。早在1943年，已由心理学家Warren S.Mcculloch和数学家Walth H.Pitts提出神经元数学模型，后被冷落了一段时间，80年代又迅猛兴起［1］。ANN之所以受到人们的普遍关注，是由于它具有本质的非线形特征、并行处理能力、强鲁棒性以及自组织自学习的能力。其中研究得最为成熟的是误差的反传模型算法(BP算法，Back Propagation)，它的网络结构及算法直观、简单，在工业领域中应用较多。-Artificial Neural Networks (Aartificial Neural Network, under the short title of ANN) is a simulation of the structure of biological neurons and the information put forward a method of treatment. At early 1943, by Warren S. Mcculloch psychologists and mathematicians Walth H. Pitts proposed mathematical model of neurons, was left out in the cold for some time, and the rapid rise of the 80" s [1]. ANN because the general concern of the people is the essence of it is because of the non-linear characteristics of parallel processing capabilities, strong robustness and self-organizing capacity of self-learning. Research one of the most sophisticated are the error-propagation model of the algorithm (BP algorithm, Back Pro

遗传算法的一个例子：旅行商问题。C++源代码，适合初学者研究遗传算法与最优化等方面的知识。-a genetic algorithm examples : the traveling salesman problem. C source code, suitable for beginners and genetic algorithm optimization and other kinds of knowledge.

测试无约束优化定理求解最小值,包括测试数据,在VC下可以实现,已经调试成功.-test unconstrained optimization for minimum theorem, including test data, the VC can be achieved, debugging has been successful.