Entropy_web_code

基于径向基神经网络的无网格法及其应用，神经网络的应用(Radial Basis Function Neural Network Based on the non-grid method and its application)

说明：  yYyong用matlab对数据的处理程序设计，极大多数的在一起。(jdfdshfsdfhsd)

此代码产生一个16QAM信号，通过高斯白噪声信道后，对其使用常模盲均衡算法，最后输出星座图和收敛图。(This code generates a 16QAM signal, through the white Gaussian noise channel, the norm for its use of blind equalization algorithm, the final output constellation map and the convergence plan.)

MATLAB简单应用，有GUI界面，有图像处理程序(Simple application of MATLAB, a GUI interface, and image processing procedures)

matlab对EXCEL进行操作，包含xlswrite函数和xlsread函数。(matlab for EXCEL operation, including xlswrite functions and xlsread function.)

进退法 罚函数法 0.618方法程序过程 分别用matlab以及c两种语言(0.618 retreat France penalty function methods and procedures to process)

Inverse dynamic analysis of 2 bar serial robot using new method De-NOC.

核主元分析和支持向量机结合的故障诊断方法(KPCA and SVM fault diagnosis method combining)

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 code make random sea wave and analysis their parameter