AWGN_VD_SPC.m

support vector classification

加入白噪声的音频水印程序，采用3级小波分解，将随机序列嵌入到低频系数中(Adding white noise of the audio watermarking procedure for the three wavelet decomposition, the random sequence is embedded into the low frequency coefficient)

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 problem is main to solve a PDE. And we have validate hte program with the analysis results.

4A finite-sample simulation study of cross validation in tree-based models. 05-15

基于线性矩阵不等式的H无穷车辆横向控制matlab仿真(Matlab simulation of H-infinite vehicle lateral control based on linear matrix inequality)

BP（Back Propagation）网络是一种按误差逆传播算法训练的多层前馈网络，是目前应用最广泛的神经网络模型之一。 BP网络能学习和存贮大量的输入-输出模式映射关系，而无需事前揭示描述这种映射关系的数学方程。 它的学习规则是使用最速下降法，通过反向传播来不断调整网络的权值和阈值，使网络的误差平方和最小。BP神经网络模型拓扑结构包括输入层（input）、隐层(hide layer)和输出层(output layer)。 通过对信息的提取以及记忆，用作分类、聚类、预测等。 (BP (Back Propagation) network is a pre-press error back propagation algorithm Multilayer feedforward network, is one of the most widely used neural network model. BP network can learn and store a large amount of input- output mode mapping without prior reveal mathematical equations that describe the mapping relationship. It s learning rule is to use the steepest descent method, by reverse spread to constantly adjust the network weights and thresholds and the minimum sum of squared error of the network. BP neural network topology comprises an input layer (input), hidden layer (hide layer) and an output layer (output layer). By extracting information and memory, it is used as classification, clustering, prediction.)

系统辨识方面的matlab程序，很快迭代算出结果(System identification in the matlab program, quickly calculate the results of iterative)

charge controller for solar photovoltaic cells pdf files

Synchronous machine PM control