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MATLAB PROJECT TO SOLVE TRANSPORTATION

matlab实现apriori算法源代码(Matlab implementation of Apriori algorithm source code )

33节点隐式高斯法程序 可计算节点电压 潮流功率分布(33 node implicit Gauss method can calculate the node voltage power flow power distribution )

this source code is eacpower matlab

实现雷达的测速和测距，并观测雷达的多普勒效应(Achieve moving target radar speed and ranging, and realize the moving target of the Doppler effect)

用MATLAB对OFDM仿真,有关于ofdm的matlab仿真(OFDM simulation using MATLAB，THIS IS ABOUT OFDM SIMULATION USING MATLAB)

对肌电信号中的有用信号的提取，将无用信号幅度变为0.(Useful for EMG signal extraction in the unwanted signal amplitude becomes 0.)

Battery Electric Vehicle and Hybrid Fuel-cell/ Battery Electric Vehicle for EPA P3 2011 Competition

交流位置伺服系统因其存在参数时变、负载扰动以及电机的非线性等缺点， 很难为其建立准确的模型. 模糊（ fuzzy）控制具有无需建立被控对象的数学模型、鲁棒性好等 优点，但稳态精度差. 将模糊控制和PID 控制相结合，设计了Fuzzy-PID 双模控制器. 在论域内 用不同的控制方式分段实现控制，这样就综合了模糊控制和PID 控制的优点，克服了各自的缺 点. 该控制器结构简单、易于实现. 在半闭环交流位置伺服系统上所做的实验表明，采用Fuzzy- PID 双模控制器与采用经典PID 控制器相比，阶跃响应的调整时间减少了33. 7 ，并且无超 调，轨迹跟踪误差减小了47. 2 .(AC servo system because of the presence of parameter variations, load disturbances and non-linear motors and other shortcomings, it is difficult to establish accurate models for fuzzy (fuzzy) control without having to build a mathematical model of controlled object, robustness and good However, steady-state accuracy is poor. fuzzy control and PID control are combined to design a dual-mode Fuzzy-PID controller in the sub-region to achieve control on the different control methods, so that a combination of fuzzy control and PID control advantages to overcome their disadvantages. simply the controller structure, easy to implement. experiments on semi-closed loop AC servo system did show that the Fuzzy-PID controller is compared with the classic dual-mode PID controller, step response The adjustment time is reduced by 33.7 , and no overshoot, tracking error is reduced 47.2 .)

将NSGA-II 的理念融入粒子群中，并加入保存优秀解的机制，提出MPSO算法。针对MPSO 易陷入局部极值的缺点，加入变邻域机制，通过随机的破坏旧解和重建新解，找到全局最优解，以达到帮助粒子跳出局部极值的目的，提 出VNS-MPSO 算法。(Will the NSGA- II concept into particle swarm, and join save good solution mechanism, MPSO algorithm is put forward. Against disadvantages of MPSO easy to fall into local extremum, join the variable neighborhood mechanism, through the random destruction of the old and rebuild the new solution, find the global optimal solution, to achieve the purpose of help particles jump out of local extremum, The VNS- MPSO algorithm.)