simple-mipv6-2

Using algorithm pso in economic dispatch 40 unit generation with wind power

漂亮的loading小控件，用的时候new一个就行了-Pretty small loading control, with a time of new on the list

一种改进的PSO，CCPSO2，该文章发表在IEEE上(An improved PSO, CCPSO2, the article was published in the IEEE)

Proteus7最新元件库及仿真实例 (解压.rarProteus7最新元件库及仿真实例(解压.rar-Proteus7 the latest example of libraries and simulation (decompression. RarProteus7 the latest example of libraries and simulation (decompression. Rar

这个代码是利用中间件RMI开发的flight航班查询，对新手的网络编程有很大的帮助，很实用的-Soil stamp Hitomi measured using tungsten Did中glass RMI Xi Jie-kai Yao flight cavity询，查beer terrace ground themis cavity PCT She met with the most bleed at the nose cavity submerged exhaust stack exhaust ， wife of mother

获取本机ip地址的3种方法。CB4.0编译-Access to the local ip address three kinds of methods. CB4.0 compiler

BP神经网络实现测试数据预测(将训练集与测试集数据进行归一化 建立BP神经网络，并训练；利用训练好的BP神经网络对测试集中的23个样本的抗压强度进行预测；输出结果并绘图)

神经网络的具体实现程序，大家学习学习，如有神经网络这方面的需求，联系我 xukai8633@126.com(Concrete realization of neural network procedures)

说明：  这是一些关于PCNN进行图像分割的Matlab程序,里面包括全部人子函数.请站长开通我的帐号,我有一个重要程序要下.谢谢!(err)

自适应GA
SVM 参数选择算法研究Param eter selection algorithm for support vector machines based on adaptive genetic algorithm 支持向量机是一种非常有前景的学习机器, 它的回归算法已经成功地用于解决非线性函数的逼近问题. 但 是, SVM 参数的选择大多数是凭经验选取, 这种方法依赖于使用者的水平, 这样不仅不能获得最佳的函数逼近效果, 而且采用人工的方法选择 SVM 参数比较浪费时间, 这在很大程度上限制了它的应用. 为了能够自动地获得最佳的 SVM 参数, 提出了基于自适应遗传算法的 SVM 参数选取方法. 该方法根据适应度值自动调整交叉概率和变异概率, 减少了遗传算法的收敛时间并且提高了遗传算法的精度, 从而确保了 SVM 参数选择的准确性. 将该方法应用于船用 锅炉汽包水位系统建模, 仿真结果表明由该方法所得的 SVM 具有较简单的结构和较好的泛化能力, 仿真精度高, 具 有一定的理论推广意义.( The support vector m achine ( SVM ) is a prom ising artificia l inte lligence technique, in w hich the regres
sion a lgorithm has a lready been used to so lve the nonlinear function approach successfully. Un fortunate ly, m ost us
ers se lect param eters for an SVM by ru le o f thumb, so they frequently fail to generate the optim al approach ing e ffect for the function. Th is has restricted effective use o f SVM to a great degree. In order to get optim a l param eters auto
m atically, a new approach based on an adaptive genetic a lgorithm ( AGA ) is presented, w hich autom atica lly ad
justs the param eters for SVM. This m ethod se lects crossover probability and mutation probab ility accord ing to the fitness va lues of the object function, therefo re reduces the convergence tim e and im proves the prec ision o fGA, in
suring the accuracy of param eter se lection. Th is m ethod w as applied to m odeling of w ater level system o f a sh ip b)