dmtteq3_1

加窗程序，包含了各种分析窗，经过调试已经通过！(Windowed program, including a variety of analysis window, after debugging has passed!)

说明：  电子工业出版社出版的《射频电路设计》，一个外国人写的，很经典的教材，这是同该书配套的光盘程序，对每一章的例题都用matlab实现。(Publishing House of Electronics Industry, )

《matlab揭秘》的各章代码与fig运行结果文件，使用matlab，这本书不错，推荐给大家下载学习。(《Matlab Reveal》 The chapters run the code and fig outcome document, use matlab, this book is good and recommend it to everyone to download learning.)

几个线性拟合的matlab程序,用到了对数，一次，二次，( several linefitng programs,matlab )

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.)

混合微粒群算法，很好很强大，欢迎交流使用(Hybrid particle swarm algorithm, very good very strong, welcome to exchange used)

Often we are interested to see how the adaptation of the coeffi cients works or how the error behaves over time. Therefore we need the function [y,e,c]=lms2(x,d,N,mu) which provides us more output arguments

说明：  matlab高效编程程序源代码第十一章，可以运行。(efficient programming source code matlab Chapter XI, you can run.)

This submission includes the presentation and model files that were used in delivering a webinar on 12-15-05 that covered the topic of modeling Hybrid Electric Vehicles. Hybrid electric vehicles (HEVs) have proven they can substantially improve fuel economy and reduce emissions. Because HEVs combine an electric drive with the internal combustion engine (ICE) in the powertrain, the vehicle?s kinetic energy can be captured during braking and transformed into electrical energy in the battery. The dual power source also means that the ICE can be reduced in size and can operate at its most efficient speeds.

Time domain FIR filter!