svm

为领域专家提供一种有效的知识获取方法。 该方法 综合各种设备故障诊断知识获取方法的优点, 引入系统论的 概念和思想, 使知识获取过程系统化和程式化, 强调和引入 了设备对象功能技术物理效应、 伴随技术物理效应和可测性 研究。 提出的设备故障诊断模型知识获取方法已应用于建立 某一抽水蓄能机组的诊断知识模型, 表现出了操作的方便 性, 较好地解决了所获取知识的完备性和一致性问题。该方 法使设备故障诊断知识获取工作向工程化的方向迈进了一步(For the domain experts to provide an effective knowledge acquisition method. The fault diagnosis method of integration of knowledge acquisition method has the advantage, the introduction of the system of concepts and ideas, so that systematic and programmed process of knowledge acquisition, stress and the introduction of a functional device object technology physical effects, physical effects associated with technology, and testing research. Fault diagnosis model proposed knowledge acquisition method has been applied to establish the diagnosis of a Pumped Storage Unit knowledge model, showing the convenience of operation, can solve the access to knowledge of the completeness and coherence. The method makes the fault diagnosis knowledge acquisition work to project a step in the direction of)

kalman滤波程序，一维信号分析，供参考(kalman filtering procedures, one-dimensional signal analysis, for reference)

classification d une image

二维全息谱，专用于旋转机械故障诊断程序！(An identification method of thermal bend is presented based on the 2D-holospectrum technique.)

详细讲述了∏/4-DQPSK调制和解调原理,并提供了matlab实现的代码(Detail the Π/4-DQPSK modulation and demodulation, and provides a matlab implementation of the code)

觉得很好的一个Matlab程序想和大家一起分享，互相学习。(A Matlab program feel good and you want to share, learn from each other.)

T=[1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1] 输入向量的最大值和最小值 threshold=[0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1] net=newff(threshold,[31 3],{ tansig , logsig }, trainlm ) 训练次数为1000，训练目标为0.01，学习速率为0.1 net.trainParam.epochs=1000 net.trainParam.goal=0.01 LP.lr=0.1 net = train(net,P,T) 测试数据，和训练数据不一致 P_test=[0.2101 0.0950 0.1298 0.1359 0.2601 0.1001 0.0753 0.0890 0.0389 0.1451 0.0128 0.1590 0.2452 0.0512 0.1319 0.2593 0.1800 0.0711 0.2801 0.1501 0.1298 0.1001 0.1891 0.2531 0.0875 0.0058 0.1803 0.0992 0.0802 0.1002 (T = [1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1] ' of the maximum and minimum input vector threshold = [0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1] net = newff (threshold, [31 3], {' tansig' , ' logsig' }, ' trainlm' ) training times for the 1000 target of 0.01 training, learning rate of 0.1 net.trainParam.epochs = 1000 net. trainParam.goal = 0.01 LP.lr = 0.1 net = train (net, P, T) test data, and training data inconsistencies P_test = [0.2101 0.0950 0.1298 0.1359 0.2601 0.1001 0.0753 0.0890 0.0389 0.1451 0.0128 0.1590 0.2452 0.0512 0.1319 0.2593 0.1800 0.0711 0.2801 0.1501 0.1298 0.1001 0.1891 0.2531 0.0875 0.0058 0.1803 0.0992 0.0802 0.1002 )

希望学习优化设计原理的同学能够好好利用这个资料啊(Want to learn the principles of optimal design students can take advantage of this information ah)

C++ simple examples for beginners -part 4

IMfluxmodel INDUCTION MOTOR