toolbox_graph

ARROW (5/20/2009) Draw a line with an arrowhead. ARROW(Start,Stop) draws a line with an arrow from Start to Stop (points should be vectors of length 2 or 3, or matrices with 2 or 3 columns), and returns the graphics handle of the arrow(s). By itself, ARROW will use the mouse to allow selection.

摘 要:基于研究了离散混沌系统的数值解法和图形仿真,同时针对离散时滞混沌系统给出了数值仿真 的程序.(Abstract: Based on the study of a discrete chaotic system of numerical solution and graphical simulation, while for discrete time-delay chaotic system a numerical simulation procedure.)

此文件包含大量matlab例程，尤其适合初学者，当学习资料在适合不过(This file contains a lot of matlab routines, especially for beginners, but when learning materials in a suitable)

说明：  扩展卡尔曼 α-β滤波器 实现低虚警下的单目标跟踪(Extended Kalman filter for single target Extended Kalman Single target tracking under low false alarm rate)

Wifi Simulator for 802.11a Simulation

需重点研究的关键问题med的快速分解hht的(emd)

BP神经网络预测公路运量 1.问题的描述 公路运量主要包括公路的客运量和公路货运量两个方面。据研究，某地区的公路运量主要与该地区的人数、机动车数量和公路面积有关，表1给出了20年得公路运量相关数据，表中人数和公路客运量的单位为万人，机动车数量单位为万两，公路面积的单位为万平方千米，公路货运量单位为万吨。 根据有关部门数据，该地区2010年和2011年的人数分别为73.39和75.55万人，机动车数量分别为3.9635和4.0975万辆，公路面积将分别为0.9880和1.0268万平方米。请利用BP神经网络预测该地区2010年2011年得公路客运量和公路货运量。 (BP neural network to predict the road traffic (1) description of the problem of road traffic including road passenger and road freight volume two. According to research, an area of ​ ​ road traffic and the number of the region, the number of motor vehicles and road area, Table 1 shows the 20 years have to road traffic-related data, the table number and road passenger traffic units for people , million two units of the number of motor vehicles, road area in square kilometers, the highway freight volume was 10 000 tons. According to relevant data, the number of people of the region in 2010 and 2011 were 73.39 and 75.55 million, the number of motor vehicles were 3.9635 and 4.0975 million units, the road area were 0.9880 and 1.0268 million square meters. Please use the BP neural network in the region in 2011, 2010 have highway passenger volume and road cargo.)

使用支持向量机进行数据的分类预测，所需样本数据较少，且预测精度高，分类效果较好。(Using support vector machines for data classification prediction, requires less sample data, and predict high precision, classification better.)

基于粒子群的神经网络优化算法的应用，在土壤水分特征曲线中的应用。(Neural network based on particle swarm optimization algorithm applied in the soil moisture characteristic curve application.)

是用于模式识别中的一种数据采样方法，此方法是把数据按一定的比例分为训练数据和测试数据。(for pattern recognition of a data sampling methods, This method is data by a certain percentage into training data and test data.)