spr.tar

一个钩子函数的实现过程-hook function of a process to achieve

遗传算法改进粒子群算法及混沌粒子群算法的源码(Improved genetic algorithm for particle swarm optimization and chaotic particle swarm optimization)

自己实现的一个简单RPC自己实现的一个简单RPC自己实现的一个简单RPC(A simple RPC own implementation，bala bala bala bala bala bala)

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)

说明：  本书由Keras之父、现任Google人工智能研究员的弗朗索瓦·肖菜（Francois Chollet）执笔，详尽介绍了用Python和Keras进行深度学习的探索实践，涉及计算机视觉、自然语言处理、生成式模型等应用。 书中包含30多个代码示例，步骤讲解详细透彻。由于本书立足于人工智能的可达性和大众化，读者无须具备机器学习相关背景知识即可展开阅读。在学习完本书后，读者将具备搭建自己的深度学习环境、建立图像识别模型、生成图像和文字等能力。 本拐适合从事大数据及机器学习领域工作，并对深度学习感兴趣的各类读者。(This book is written by Francois Chollet, the father of keras and currently a researcher of Google artificial intelligence. It introduces in detail the exploration and practice of deep learning with Python and keras, involving computer vision, natural language processing, generative model and other applications. The book contains more than 30 code examples, the steps to explain in detail. Because this book is based on the accessibility and popularity of artificial intelligence, readers can read without the background knowledge of machine learning. After learning this book, readers will have the ability to build their own deep learning environment, establish image recognition model, and generate images and characters. It is suitable for all kinds of readers who are engaged in big data and machine learning and are interested in deep learning.)

利用粒子群算法对最小二乘支持向量机进行优化。(The least squares support vector machine is optimized by particle swarm optimization.)

说明：  keras 和tensorflow用GAN实现时间序列预测(Time series prediction with GaN by keras and tensorflow)

socketio for client socketio for client socketio for client socketio for client

是个实现简单抓包，在运用于网络或单机中的实现。(is simple-capturing Packet achieved in networks or single-board computers used in the implementation.)

  n开发一个以太网帧解析程序，实现以下功能：解析已封装好的Ethernet V2.0帧封装文件（见附件），判断有多少个帧，每个帧的所有字段内容，检测CRC 请点击左侧文件开始预览 ！预览只提供20%的代码片段，完整代码需下载后查看 加载中 侵权举报 打分 发表评论 暂无评论