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在[0 2π]范围内绘制以Y轴为对数的二维曲线图， y=|1000sin(4x)|+1(describe )

一种噪声辅助数据分析方法,基于人工神经网络的常用数字信号调制，信号维数的估计。( A noise auxiliary data analysis method, The commonly used digital signal modulation based on artificial neural network, Signal dimension estimates.)

说明：  一个自己用的谱聚类算法函数，输入距离矩阵和聚类数，输出类别(Spectral Clustering Method)

源码是BPSK单载波调制方式下对整个系统的调制与解调(Source is a single carrier BPSK modulation of the system under modulation and demodulation)

正交频分复用源程序，交含逆快速傅立叶变换，ISI，傅立叶变换(main OFDM)

说明：  一个对数据进行独立成分分析的小程序，可对统计数据进行快速的降维。(An independent component analysis of the data of small procedures, statistical data on the fast dimension reduction.)

关于Matlab的课件，里面含有数学建模的基础知识，对于数学建模的了解是很有帮助的(Courseware on Matlab, which contains the basics of mathematical modeling, mathematical modeling for understanding is helpful)

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.)

各种数据拟合的程序，对初学者很有用。可以做各种图(A variety of data fitting procedure, useful for beginners.)

在matlab中，采用模糊神经网络对监控水资源(In matlab using fuzzy neural network monitoring water)