codePhase1

Free Space Path Loss Model

matlab exercise hope can be useful

matlab file for cample bozorgnaia 2004 attenuation code

分析了支持向量回归机在能源需求预测中的优势,确定了输入向量集合和输出向量集合,建立了基于Matlab技术的SVR能源需求预测模型.对我国1985-2008年能源需求相关数据进行模拟与仿真,并对中国2010年和2020年能源需求量进行预测.研究结果表明:一是中国未来对能源的需求量逐渐增加,从2010年的330400万吨标准煤上升到2020年418320万吨标准煤,年均增长率为2.39％；二是在解决我国能源系统小样本.非线性及高维模式识别问题中SVR比BP神经网络等方法有更高的预测精度.(Support vector regression analyzes advantage in energy demand forecast to determine the set of input vectors and output vector set, established SVR energy demand forecasting model based on Matlab technology. Energy demand for our 1985-2008 related data modeling and simulation , and China in 2010 and 2020 energy demand forecast results show that: First, China s increasing demand for energy in the future, from 3.304 billion tons of standard coal in 2010 rose to 4.1832 billion tons of standard coal in 2020, average annual growth rate of 2.39 second is to solve our energy system small sample nonlinear and high dimensional pattern recognition problem SVR higher prediction accuracy than the BP neural network method.)

抛物方程能够快速对大区域电波传播特性做出预测，目前已经得到了广泛的应用。压缩包里给出了二维抛物方程的matlab程序(The parabolic equation can quickly predict the propagation characteristics of large region waves. It has been widely used at present. In this paper, a two-dimensional parabolic equation program is given in this paper.)

小波灰度矩向量计算代码，首先进行morlet连续小波变换，然后将小波灰度矩分为16份(Wavelet gray moment vector calculation code, the first to morlet continuous wavelet transform, and then divided into 16 wavelet Gray Moment)

Character recognization

下载比特币的全套数据, 以txt文件输出~~每一个txt文件就是一个block的数据( to download the transaction data of BTC)

sapjco3官网jar包，包中含有详细开发实例(sapjco3 official website of the jar package, the package contains a detailed example of the development)

This paper presents an overview of our most recent results concerning the Particle Swarm Optimization (PSO) method. Techniques for the alleviation of local minima, and for detecting multiple minimizers are described. Moreover, results on the ability of the PSO in tackling Multiobjective, Minimax, Integer Programming and
1 errors-in-variables problems, as well as problems in noisy and continuously changing environments, are reported. Finally, a Composite PSO, in which the heuristic parameters of PSO are controlled by a Differential Evolution algorithm during the optimization, is described, and results for many well-known and widely used test functions are given.