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本文对倒立摆系统的Lagrange方程建模与模糊控制进行了简单介绍(In this paper, the inverted pendulum system Lagrange equation modeling and fuzzy control for a simple introduction)

palm print recognition project did in matlab

说明：  通过迭代的方法来计算物质的光学性质，通过最小二乘法来设置收敛的条件，当结果小于收敛条件时，输出结果(Calculate liquid refractive index, using the iteration method, design the smallest error by the least squares method, to terminate the iteration convergence )

此程序主要是通过使用小波达到音频压缩的目的，可以比较不同的小波函数和压缩次数音频文件的差异(This process is mainly achieved through the use of wavelet purpose audio compression, you can compare different compression of the wavelet function and the difference in the number of audio files)

使用matlab实现zernike模板系数的求解，避免了人工计算的麻烦与复杂(Using matlab to achieve the solution of coefficient zernike template to avoid the trouble of manual calculation and the complexity of)

matlab simulink implementation and analysis of Three-phase inverter4

该工具箱为时频分析中的一类重要变换,离散时频分布的工具箱,其中提供基本算法的程序.可直接应用(the Toolbox too frequency analysis of a kind of important transformation, discrete time-frequency distribution of the toolkit, providing basic arithmetic procedures. can be directly applied)

各种调制方式的性能分析及其对比可以很直观的得到常见的几种调制方式的性能(Performance analysis and comparison of various modulation schemes)

matlab7.0教学。。。。。。。。。。。。。。。。。。(matlab7.0。。。。。。。。。)

These files contain all the code necessary to run the example in the Webinar "Signal Processing for Machine Learning in MATLAB". They also include code to automate the download and preparation of the dataset used. In that webinar we presented an example of a classification system able to identify the physical activity that a human subject is engaged in, solely based on the accelerometer signals generated by his or her smartphone. We used consolidated signal processing methods to extract a fairly small number of highly-descriptive features, and finally trained a small Neural Network to map the feature vectors into the 6 different activity classes of a pre-recorded dataset. The topics discussed include: * Signal manipulation and visualisation * Design and application of digital filters * Frequency-domain analysis * Automatic peak detection * Feature extraction from signals * Train and test of simple Neural Networks