有关遗传算法的原理与应用 不错啊分享一下啊

遗传算法在工程优化计算上的应用的MATLAB程序。-genetic algorithm optimization calculation of the MATLAB program.

一个利用vc编成的倒传递类神经网络内容包含到数层的神经网络编写 使用者可以清楚了解BP- Contains using the vc forming but actually transmission class nerve network content to several nerve networks compiles the user to be allowed clearly to understand BP

模式识别中的ISODATA算法演示-pattern recognition algorithms demonstrated the ISODATA

workbench的应用开发-application development workbench

利用a星算法来走迷宫的C++实例。console下运行，a星算法比较简洁明了，便于学习-The use of a satellite algorithm to Maze in C++ examples. console running, a star algorithm is relatively simple and clear, easy to learn

用Fast BP实现复杂的函数逼近,对初学者有一定的帮助,是在BP的基础上增加了R.-Fast BP achieved with a complex function approximation, there is some help for beginners, is based on the BP increase in R.

汉字识别的一个小例子 识别率不是那么高，但还是可以借鉴的-one small example of the recognition rate is not so high, but still useful

基于canny的灰色预测和模式识别,标定的一种做法是：在一块平板上画若干个点， 同时用这两部相机照相，分别得到这些点在它们像平面上的像点，利用这两组像点的几何关系就可以得到这两部相机的相对位置。然而，无论在物平面或像平面上我们都无法直接得到没有几何尺寸的“点”。实际的做法是在物平面上画若干个圆（称为靶标），它们的圆心就是几何的点了。而它们的像一般会变形，如图1所示，所以必须从靶标上的这些圆的像中把圆心的像精确地找到，标定就可实现。-Based on the gray canny prediction and pattern recognition, calibration approach is to: a plate painted in a number of points, at the same time with these two cameras camera, respectively, as these points in their plane as points, the use of these two groups geometric relationship between the image point can be that the relative positions of two cameras. However, in terms of objects such as plane or plane, we can not directly be no geometry point. Actual practice is complex plane painted in a number of round (known as targets), their geometric center of a circle is the point. And they generally like deformation, as shown in Figure 1, it must target these round, like in the center of a circle of like to find precise, calibration can achieve.

利用人工智能原理中BP神经网络算法，建立的彩票预测程序，当然不可能都预测中，但是要学习神经网络的人值得一看-BP neural network algorithm, the establishment of the lottery forecasting process, of course, not all forecasts, but neural networks to study the eye-catcher

多步预测,设计简单规范,基于信息建模,具有很强的抗干扰性和鲁棒性.-Multi-step prediction, the design of simple norms, based on information modeling, which has strong anti-jamming and robustness.