AdaptiveLineEnhancer1

m file pattern kn-nigber-classifunction

三个耦合区的微环谐振器的滤波特性matlab仿真(Microring resonator filter characteristic three coupling region)

MATLAB code for root locus plot

基于matlab图像处理实现螺纹识别，滤波，边缘检测，拟合数据(Based on MATLAB image processing, thread recognition, filtering, edge detection and fitting data are realized.)

说明：  这个程序是我自己编写的小车倒立摆经典PID控制和模糊控制matlab程序，相信对初学模糊控制的朋友有不小的帮助，包含传统PID控制和模糊控制的对比，是一份内容详细的完整报告(This program is written in my own car classic inverted pendulum PID control and fuzzy control matlab program, I believe beginner friends of fuzzy control no small help, with the traditional PID control and fuzzy control of contrast, is a complete and detailed report of)

用于混沌时间预测与分析，经调试，可以运行(Used for prediction and analysis of chaotic time, debugging, you can run)

基于模糊控制的PID温度控制器的设计 :将模糊控制和PID 控制相结合起来，通过模糊控制实现PID 参数自适应的方法来控制系统温度。这种Fuzzy- PID 策略，模糊控制的采用不是代替PID 控制，而是对传统控制方式的改进和扩展，它既保持了常规PID 控制系统结构简单、使用方便、鲁棒性强、控制精度高的优点，又采用模糊推理的方法实现了PID 参数PK、IK、DK的在线自整定，兼具了模糊控制灵活性、适应性强的特点，相比单纯的任一种控制效果都要好(PID temperature controller design based on fuzzy control: fuzzy control and PID control combine to achieve PID adaptive approach to control system temperature by means of fuzzy control. This Fuzzy- PID strategy is not to replace the use of fuzzy control PID control, but the traditional control methods to improve and expand, both to maintain a simple and conventional PID control system structure, easy to use, robust, and high control accuracy advantages, but also the use of fuzzy inference PID parameters to achieve the PK, IK, DK-line self-tuning, both the fuzzy control flexibility, adaptability features, compared to simply have a good control effect either)

利用蝶形算法，实现快速傅立叶变换及其逆变换(The use of butterfly algorithm, fast Fourier transform and its inverse transform)

闭环伺服系统传递函数和数学模型的研究，做电机伺服的可以看看 (closed-loop servo system transfer function and the mathematical model studies, servo motor can see)

In this paper a Conventional PD Controller and Fuzzy Neural Controller for trajectory tracking of mobile robot platform is presented. Using computed torque method is given input- torque on mobile robot wheels. The locomotion control structure is based on integration of kinematics and dynamics model of mobile robot base. The proposed Control scheme and Fuzzy Neural Algorithm could be useful for building an autonomous non-destructive testing system based on wheeled mobile robot. Fuzzy Neural Network-Rule base, is optimized using Genetic Algorithm. Simulations are done using Matlab software. Key words:Mobile Robot, Conventional, Fuzzy Logic, Neural Network, Genetic Algorithm, trajectory tracking,