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LMS_gai2
两个频率的凹口滤波对消干扰噪声，效果很好。希望大家仿真实验。(Two frequency notch filter to eliminate interference noise, the effect is very good. I hope everyone simulation experiments.)
- 2013-03-10 18:59:46下载
- 积分：1
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takhasosikhorojii
neural network pro.output learning
- 2014-11-20 05:13:00下载
- 积分：1
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ronghe
两个传感器进行串联形式连接，融合系统贝叶斯风险曲线以及ROC曲线(Two sensors are connected in series, as well as fusion system Bayesian risk curve ROC curve)
- 2013-10-08 23:45:31下载
- 积分：1
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linear_system_identification.tar
The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order (The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order)
- 2008-08-03 10:18:16下载
- 积分：1
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3rid_cyclic_cumnunt
说明： 在工程里识别不同FSK和PSK的三阶循环特性的程序，其中最著名的算法！看通信调制识别的人一定看得懂(In engineering in different FSK and PSK to identify the third-order cycle characteristics of the procedures, most notably the algorithm! Modulation recognition of communication to see people must understand)
- 2008-10-18 22:30:31下载
- 积分：1
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gui_camera
bean recog -gui red , green and black color only M file
- 2013-07-10 05:44:17下载
- 积分：1
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fgd
基于MATLAB的，交互式多模型算法卡尔曼滤波仿真代码(MATLAB-based, interactive multiple model algorithm for Kalman Filter Simulation Code)
- 2009-11-15 19:23:49下载
- 积分：1
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OPC
OPC DA 3.0 specification
- 2014-10-20 11:45:34下载
- 积分：1
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LFM-CW-Radar
本文从理论上对LFM-CW 雷达进行了信号分析，并对LFM-CW 雷达的信号
处理方法进行了简单研究，最后通过仿真程序验证了该信号处理方法（距离/多普勒处理）
在计算运动目标距离和速度时的正确性，为LFM-CW 雷达信号处理提供了理论依据。(This paper analyze the signal of LFM-CW radar by theory, and research the
signal process method of LFM-CW radar. In the end, this paper validate the signal process
method’s correctness to calculate the moving target’s distance and speed by emulate program. This
paper offer the theory for the signal process method of LFM-CW radar.)
- 2012-09-26 17:50:16下载
- 积分：1
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NSGA-III
测试可以跑，根据自己情况修改下函数即可. NSGA-III 首先定义一组参考点。然后随机生成含有 N 个（原文献说最好与参考点个数相同）个体的初始种群，其中 N 是种群大小。接下来，算法进行迭代直至终止条件满足。在第 t 代，算法在当前种群 Pt的基础上，通过随机选择，模拟两点交叉（Simulated Binary Crossover，SBX）和多项式变异 产生子代种群 Qt。Pt和 Qt的大小均为 N。因此，两个种群 Pt和 Qt合并会形成种群大小为 2N 的新的种群 Rt=Pt∪Qt。 为了从种群 Rt中选择最好的 N 个解进入下一代，首先利用基于Pareto支配的非支配排序将 Rt分为若干不同的非支配层（F1，F2等等）。然后，算法构建一个新的种群St，构建方法是从 F1开始，逐次将各非支配层的解加入到 St，直至 St的大小等于 N，或首次大于 N。假设最后可以接受的非支配层是 L层，那么在 L+ 1 层以及之后的那些解就被丢弃掉了，且 St FL中的解已经确定被选择作为 Pt+1中的解。Pt+1中余下的个体需要从 FL中选取，选择的依据是要使种群在目标空间中具有理想的多样性。(The test can run and modify the function according to its own situation. NSGA-III first defines a set of reference points. Then the initial population containing N individuals (preferably the same number of reference points as the original literature) was randomly generated, where N was the size of the population. Next, the algorithm is iterated until the termination condition is satisfied. On the basis of current population Pt, the algorithm simulates two-point crossover (SBX) and polynomial mutation to produce offspring population Qt by random selection.)
- 2021-01-26 22:38:41下载
- 积分：1
