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说明： 设计巴特沃斯型、椭圆、切比雪夫数字低通滤波器，并对加入噪声的图像进行滤波(design Buttetworth、cheby1 and ellip LF,and use these filters in image processing.)
- 2011-05-17 16:05:50下载
- 积分：1
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eg28-dingdanxuqiuyuce
《MATLAB神经网络30个案例分析》中的第28个例子，案例28 灰色神经网络的预测算法—订单需求预测。希望对大家有一定的帮助！(The MATLAB neural network analysis of 30 cases of 28 example, 28 cases of grey neural network prediction algorithm- order demand forecasting. Hope to have certain help to everybody!
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- 2013-09-20 16:46:33下载
- 积分：1
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chebyshev_lowpass_filter.m
4th order chebyshev fiter for a given Q and freq
- 2012-02-15 16:52:49下载
- 积分：1
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pid
主要研究基于粒子群算法控制系统PID参数优化设计方法以及对PID控制的改进。选择控制系统的目标函数，本控制系统选用时间乘以误差的绝对值，通过对控制系统的逐步仿真,对结果进行分析(The main research based on particle swarm optimization algorithm control system PID parameter optimization design method and the improvement of PID control. Choose the objective function of the control system, this control system uses the absolute value of the error, and the result is analyzed by the simulation of the control system.
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- 2016-09-18 09:02:53下载
- 积分：1
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GPC
说明： 广义隐式预测控制MATLAB实现,利用最小二乘法在线辨识(The generalized implicit predictive control is realized by MATLAB and identified online by least square method)
- 2020-05-26 09:08:01下载
- 积分：1
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faultanalysis_adaptive_filter
Fault analysis using full wave window fourier
- 2010-10-16 10:30:28下载
- 积分：1
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C3
说明： MATLAB的基础模糊c-均值算法，可以直接运行(fuzzy clusting)
- 2009-11-21 10:17:22下载
- 积分：1
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genzong
提供直扩系统的跟踪环路的matlab程序，并对其误差进行分析(Direct expansion system to provide tracking loop matlab program, and its error analysis)
- 2020-12-14 13:59:14下载
- 积分：1
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STATCOM-HysteresisCurrentControl-Technique
This is the matlab simulation of STATCOM using Hystersis control Technique
- 2011-12-16 03:16:16下载
- 积分：1
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K-meanCluster
How the K-mean Cluster work
Step 1. Begin with a decision the value of k = number of clusters
Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following:
Take the first k training sample as single-element clusters
Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster.
Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample.
Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments. (How the K-mean Cluster workStep 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (Nk) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3. Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4. Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.)
- 2007-11-15 01:49:03下载
- 积分：1
