MATLAB符号运算及其应用

vb调用matlab——多项式拟合 vb调用matlab——多项式拟合(vb call matlab- polynomial fitting vb call matlab- polynomial fitting)

想保留的成分为cos(2*π*n/3) 带通滤波器，滤除1+cos(2*π*n/3) 的成分，即想保留的成分为cos(π*n/4) 带阻滤波器，滤除cos(π*n/4) 的成分，(Want to keep the ingredients for the cos (2* π* n/3) band-pass filter, filter 1+ Cos (2* π* n/3) of the composition, that is, want to keep the ingredients of cos (π* n/4 ) band-stop filter, filter cos (π* n/4) of the composition,)

MATLAB中也同样有许多有意思的实例，为提高读者对MATLAB和Simulink的兴趣，特举部分以供参考。(MATLAB, there are also many interesting examples, in order to enhance the reader' s interest on MATLAB and Simulink, special give some for reference.)

根据小波的分解算法与重构算法来实现小波的分解与重构(Algorithm based on wavelet decomposition and reconstruction algorithm to achieve the wavelet decomposition and reconstruction)

利用模拟退火法求函数在某一范围的极小值，对于验证模拟退火算法的可行性有重要意义。(Using simulated annealing method the minimum value of a function in a range of simulated annealing algorithm to verify the feasibility of significance.)

反问题，由采集的温度场计算三种组分的混合物的导热系数和热容(Inverse problem, the temperature field calculation by the acquisition of a mixture of three components of thermal conductivity and heat capacity)

Estimation, Control, and the Discrete Kalman Filter By:Donald E. Catlin

在机动目标跟踪中，机动目标模型是机动目标跟踪的基本要素之一，一般希望机动目标模型能准确表征目标机动时的各种运动状态。比较常用的模型有匀速运动(CV)模型、匀加速运动(CA) 模型、时间相关模型（Singer）和机动目标“当前”统计模型。上述模型均采用机动频率表征目标的机动情况。在应用当中，通常采用固定的机动频率，这就表示机动目标的机动时间是一定的，而实际上机动目标的机动时间是不断变化的，也就是说机动频率是不断变化的，采用固定机动频率必然会带来误差。采样周期在0.5—2S时，机动频率越小跟踪精度越高[1]，但机动频率仍然是固定值。本文提出的基于神经网络的机动频率自适应调整方法可以使机动频率随机动而变化，从而提高状态估计的准确性，提高跟踪精度。本文将小波神经网络用于机动目标跟踪中机动频率的自适应调整，该算法对机动目标“当前”统计模型中的机动频率进行实时修改, 从而自适应的改变机动频率,使跟踪算法与目标的真实状态更接近。该算法采用小波神经网络的离线训练，实时性好。(The maneuver of the maneuvering target is uncertain. The maneuvering frequency is constantly changeable, but traditionally it is beforehand determined as a constant based on the target state estimation in the state model of the maneuvering target. The maneuver of the maneuvering target makes the kinematics equation of the target model mismatch with the practical motion model and the tracking error will be increased. Based on the advantages of the self-learning, the rapid convergence rate and the nonlinear approximation ability of the wavelet neural network, it was put forward to be used in the field of target tracking in the paper. The new residual is used as the input of the wavelet neural network, the output of the network is used to adjust adaptively the maneuvering frequency of the CS model. The algorithm is more close to the real state of the target. The simulation results showed that tracking error can be reduced and the tracking accuracy can be improved.)

This is the program which is responsible for anderson darling k sample test

读取以.dat的数据文件；读取以MIT-BIH数据库的212格式文件(Read the MIT-BIH database 212 format data)