dhcp-Technical-White-Paper

Glvq神经网络实现对火焰的特征提取和训练的算法(Glvq for flame feature extraction and neural network training algorithm)

andrew NG上的课程machine learning 的习题解答，这些习题对于机器学习入门还是非常有帮助的，值得学习。(This my answer to the programming exercises of Machine Learning by Andrew NG,this files are very useful to the people who want to study with Machine Learning.)

1.-Realizar 联合国方案委托/Servidor utilizando 套接字 TCP，el cual realizará funciones similares las 德联合国聊天。 2.-El servidor 无 tendrá interfaz、 ni mostrará ningun 提珀德信息 en la consola。段 escuchar，埃斯特角 utilizará el 波多黎各 número 4950 y podrán conectarse clientes desde cualquier interfaz que tenga configurada el 法医。 3.-埃斯特角方案 deberá atender tantos clientes 科摩 se conecten al mism tiempo。 4.-En el 委托 deberán establecerse los parametros necesarios 段

利用网上资源，根据IP地址查找物理地址，供大家参考，原理非常简单，就是访问下网页(Use of online resources to find the physical address based on the IP address)

一个蜘蛛程序源-spiderman

提出了一种基于自适应 Chebyshev 多项式神经网络(ACNN)的 Logistic 混沌系统控制算法。该算法采用 Chebyshev 正交多项式作为神经网络的激励函数, 构建 Logistic 混沌系统的预测与控制模型。为了保证算法的稳定性, 提出和证明了收敛定 理, 并利用自适应学习率算法提高神经网络的学习效率和收敛速度。通过采用自适应 Chebyshev 神经网络直接学习 Logistic 混 沌系统的动态特性, 并对系统实施目标函数控制。实验仿真结果表明, 该算法在 Logistic 混沌系统有外部干扰的情况下仍能对其 进行有效控制, 网络学习时间为 0.178 s, 训练步长为 10, 均方误差达到 1.15×10 − 4 , 与其他常见算法相比具有计算量小、速度快、 精度高和网络结构简单等优点。 ( A novel algorithm for controlling Logistic chaotic system based on adaptive Chebyshev polynomials neural networks (ACNN) is presented. In the algorithm, the activation function of hidden units is defined by Chebyshev orthogonal polynomials in the neural networks, and the forecast and control model of Logistic chaotic system is estab- lished. In order to ensure stability of the algorithm, the convergence theorem of the algorithm is proposed and proved. Then the adaptive learning rate algorithm is used for improving the learning efficiency and convergence speed. The adaptive Chebyshev neural networks directly learn dynamic characters of Logistic chaotic system and control it to target function. The simulation results show that the algorithm is still effective when there are external disturbance in the Lo- gistic chaotic system, now the learning time is 0.178s, training steps is 10 and mean square error is 1.15×10 − 4 . Com- pared with other ordinary )

说明：  神经网络学习，深度学习，人工自然神经网络(Neural network learning)

基于风速预测的风力发电平均功率率侧，神经网络算法(Side of the neural network algorithm based on wind speed forecast wind power average power ratio)

这是一个用c#做的聊天室,有服务器和客户端,采用多线程技术-This is a c# to do with the chat rooms, there are server and client, using multi-threading technology

基于lenet-5模型，利用CNN进行图像分类(Image classification)