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A PID Control for Improving the characteristics of a Quarter model of a car suspension

说明：  数值分析方法-数据拟合-多项式数据拟合的实现(Numerical analysis method- data fit- polynomial data fitting to achieve)

LCL型滤波并网逆变器双环控制参数设计与优化，对工程实践的指导作用明显(LCL type filter and inverter loop control parameter design and optimization, the guiding role of engineering practice significantly)

matlab 中用Bisection方法求解一元多次方程，是一种很好的算法。但收敛速度比较慢，是线性收敛(matlab using Bisection method for solving multiple equations one yuan, is a good algorithm. But convergence is slow, linear convergence)

Tretrans-this file_simulates transmiter in relay node of cooperative ofdm

This is predictive simulation file. It contations too many mdl files. and it has predictive+802.11a simulink. Thank you

使用matlab编写的PSO算法，求解函数最优值(using matlab to realize particle swarm optimization algorithm for optimization)

m_map是加拿大学者编写的一个在matlab上绘地图的软件，对于海洋遥感的研究有用。(m_map is written by Canadian scholars which can be used to draw a map in matlab programming environment. This software is very useful especially for ocean science research.)

use matlab to control mouse

GM(1,1)模型1-4 1：GM(1,1)模拟模型，在matlab中的输入方法为gm1（x），x指要模拟的序列。 2：GM(1,1)预测模型，在matlab中的输入方法为gm2（x，K），x指要模拟的序列，K指从以后序列第一个数据算起的第k个待预测数据。 3：GM(1,1)群模拟模型，在matlab中的输入方法为gm3（x），x指要模拟的序列。 4：GM(1,1)群预测模型，在matlab中的输入方法为gm4（x，K），x指要模拟的序列，K指从以后序列第一个数据算起的第k个待预测数据。 gm4对序列趋势比较好的数据预测效果较好，对上下变动的数据，特别是后4个数据趋势跟前面的数据相反的，预测效果很差。 gm2对上下变动的数据，预测效果比gm4好，但对趋势较好的数据，预测精度没有gm4高。 gm3比gm1模拟精度要高。 可以以x=[1 3 5 7 9 11 13 15]进行实验。x输入默认行向量。 所有程序在matlab6.0上调试通过。(GM (1,1) 1-4 1 : GM (1,1) simulation model Matlab in the input method for gm1 (x), x entail simulation sequence. 2 : GM (1,1) model, in the Matlab input method for gm2 (x, K), x entail simulated sequence, from the K refers to the first series after a run of data k pending forecast data. 3 : GM (1,1)- simulation model, in the Matlab input method for gm3 (x), x entail simulation sequence. 4 : GM (1,1)- forecasting model, in the Matlab input method for gm4 (x, K), x entail simulated sequence, from the K refers to the first series after a run of data k pending forecast data. Gm4 right sequence relatively good trend data predicted better results, the next change in the data, especially after four data with the previous trend data contrary, predict poor. Gm2 next to the change in forecast resu)