MedianFilter

最小二乘法推广为一种自适应算法，这种自适应的算法称为RLS算法。(The promotion of the least square method for an adaptive algorithm, such as the RLS algorithm for adaptive algorithms.)

函数数据工具箱，包括FPCA,回归，微分方程拟合(Function of data toolbox, including the FPCA, regression，differential equations fitting)

介绍了我们常用的预测方法神经网络算法的理论知识(Common prediction methods of theoretical knowledge of the neural network algorithm)

基于matlab的4种梯度算子，Sobel Operator、Laplacian Operator、Prewitt Operator等等(Matlab based on the four kinds of gradient operator)

project for student information system

本书介绍了matlab guide的设计方法, 讲解的非常清楚,包含了大量的实例程序,对于全面了解和掌握matlab 界面设计有很大的参考价值(book on Matlab guide the design method, on the very clear, There are a lot of examples of procedures for a comprehensive understanding and grasp of Matlab interface design is a great reference value)

基于功率谱相减的语音消噪增强法程序，采用硬阈值法(Power spectrum subtraction based speech denoising Enhancement Act program, using hard-threshold)

光伏并网lcl滤波器，可以进行仿真有波形 (Photovoltaic lcl filter, the waveform can be simulated)

利用Capon, MUSIC进行窄带信号空域信号方向估计。比较分辨率性能。观察阵元数、信噪比、快拍数对估计器性能的影响。 (Using Capon, MUSIC for narrowband signal airspace signal direction estimates. Comparative resolution performance. Observe the number of array elements, signal to noise ratio, the number of snapshots on the estimator performance.)

Elman神经网络：Elman网络是 J. L. Elman于1990年首先针对语音处理问题而提出来的, 它是一种典型的局部回归网络( global feed for ward l ocal recurrent)。Elman网络可以看作是一个具有局部记忆单元和局部反馈连接的前向神经网络。Elman网络具有与多层前向网络相似的多层结构。它的主要结构是前馈连接, 包括输入层、 隐含层、 输出层, 其连接权可以进行学习修正 反馈连接由一组“结构 ” 单元构成,用来记忆前一时刻的输出值, 其连接权值是固定的。在这种网络中, 除了普通的隐含层外, 还有一个特别的隐含层,称为关联层 (或联系单元层 ) 该层从隐含层接收反馈信号, 每一个隐含层节点都有一个与之对应的关联层节点连接。关联层的作用是通过联接记忆将上一个时刻的隐层状态连同当前时刻的网络输入一起作为隐层的输入, 相当于状态反馈。隐层的传递函数仍为某种非线性函数, 一般为 Sigmoid函数, 输出层为线性函数, 关联层也为线性函数。(Elman neural network: Elman JL Elman network is first proposed for speech processing problems in 1990. It is a typical local regression network (global feed for ward l ocal recurrent). Elman network can be seen as having a forward neural network of local memory unit and local feedback connections. Elman network has a multi-layer multilayer structure similar to the network. Its main structure is a feedforward connection, comprising an input layer, a hidden layer and output layer, which can be connected right learning correction feedback connected by a set of " structural" units, the output value of the memory for the previous time, which connection weights are fixed. In such a network, in addition to ordinary hidden layer, there is a special hidden layer, called an associative layer (or contact layer unit) this layer receives feedback signals the hidden layer, each hidden layer node has an associated layer node corresponding connection. The role of the associated layer is fo)