FarsiGuidetoANNToolboxMLPwithProg

通过一个简单的例子来讲解遗传算法，适合初学者学习领悟遗传算法的真谛。(Through a simple example to explain the genetic algorithm, genetic algorithm is suitable for beginners to learn comprehend the true meaning.)

详细的介绍MATLAB 7 的功能，运用源代码和实例进行解释，方便的边学便实践。(described in detail the function of MATLAB 7, the use of the source code and examples to explain, facilitate the learning they practice.)

网上到处都是一维k均值聚类，而这是一个二维k均值聚类的代码，老外写的，说明较详细，稍加修改就可以融入自己的项目中。(Everywhere online one-dimensional k-means clustering, which is a two-dimensional k-means clustering code, written by foreigners, the more detailed, slight modifications can be integrated into the project.)

心电信号压缩，利用小波理论，基于小波变换的心电信号的压缩。(Ecg signal compression, using the theory of wavelet, the compression of ecg signals based on wavelet transform.)

说明：  这是一本将合成孔径雷达最权威也是最全的教科书，大家可以下载下来看看(This is a synthetic aperture radar will be the most authoritative and most all of the textbooks, you can download to see)

SOURCE CODE FOR MATLAB

用matlabr2009编程实现神经网络模糊聚类算法，采用cell工具把整个程序分为以下几个部分(Programming neural network with matlabr2009 fuzzy clustering algorithm, using cell tools to the entire program is divided into the following sections)

QPSK的调制；比较DQPSK和QPSK在通过加性高斯白噪声信道时的误码性能；OFDM系统(QPSK modulation more DQPSK and QPSK in additive white Gaussian noise through the channel bit error performance when OFDM system)

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.)

单电磁矢量传感器噪声子空间拟合算法-空间谱图(Single electromagnetic vector sensor noise subspace fitting algorithm- space spectra)