euler

This folder contains presentation file on electrostatic discharge and its adverse effects.Then this folder contains my matlab final project report and the project title was designing butterworth filters using matlab.Then this folder also contain one main title page that can be used for reports title page after some formating.

The function of channel estimation is to form an estimate of the amplitude

The function of channel estimation is to form an estimate of the amplitude

数据分析与解方程，绘图，插值，拟合，以及一些经典的matlab算法程序(Data analysis and solution of equations, mapping, interpolation, fitting, as well as some classic algorithm matlab program)

对带磁滞环的驱动器进行建模，可用于模型估计(Prandtl-Ishlinskii hysteresis model for hystersis in actuator)

神经网络PID自适应控制MATLAB程序集(the collect of the neural network PID adaptivly controls MATLAB procedure)

codes for kmean method

说明：  此源码是对人工鱼群算法的一种实现，用于无约束连续函数的优化求解，对于含有约束的情况，可以先使用罚函数等方法，把问题处理成无约束的模型，再使用本源码进行求解。仅供学习参考。(This source is a realization of artificial fish school algorithm for unconstrained optimization of continuous functions to solve for the case with constraint, you can first use the penalty function and other methods to deal with the problem into the unconstrained model, then use the source to solve it. Study reference purposes only.)

提出了一种新的算法结构,通过建立“局部环境因数”模型,利用集中式处理模式,动态 分配全局勘探和局部开采子种群比例,有效地实现分工目的,平衡算法的局部和全局搜索能力。将其 应用到两个不同类型的实际工程约束优化问题中进行验证,并与其他文献的改进算法进行了对比。 实验结果表明,该算法比其他改进算法在计算精度、效率、鲁棒性上都有很大的提高(Anewalgorithmarchitecturewasproposed. Inorder to adjust effectively the ratioof exploration subgroup versusexploitationone, the algorithmadopted the centralizedprocessingtechnique toconstruct the local environment factor, andbalancedthe local andglobal searchcapabilitiesof the algorithm. Furthermore, comparedwithother improvedintelligent algorithms, experimental results got from the application and verification of real constrained engineering design problem indicate that the algorithmperformsbetter intermsof accuracy, efficiencyandrobustness)

This project is based on group human activity classification using matlab