NOMA-OFDMA-SIM

遗传算法例程：通过MATLAB，以航班调度排序为例，实现遗传算法。(Genetic algorithm routines: the adoption of MATLAB, in order to sort Flight Scheduling as an example, the realization of genetic algorithm.)

觉得很好的一个Matlab程序想和大家一起分享，互相学习。(A Matlab program feel good and you want to share, learn from each other.)

最小均方误差均衡器，误码率和信噪比的关系曲线(Minimum mean square error equalizer, the relationship between BER and SNR curve)

张量处理工具箱，包括了基本运算，有示例说明，很容易学懂，可以应用在信号处理领域(Tensor toolbox for signal processing)

A SISO 20M OFDM system based on IEEE802.11n standard, using Matlab.

MIMO-OFDM自适应实现，如果有自适应调制,进行逐用户,逐OFDM符号,逐子载波的星座调制(MIMO-OFDM Adaptive be achieved if there is an adaptive modulation performed by the user, per OFDM symbol-by-subcarrier modulation constellation)

贝叶斯网络的matlab实现。可以创建贝叶斯网络、训练模型，以及实现统计推理。这里是最新的1.0.4版(Bayesian networks to achieve matlab. Bayesian networks can be created, the training model, and the achievement of statistical inference. Here is the latest version 1.0.4)

说明：  利用matlab实现了遗传算法，并利用该算法对一般的连续函数进行了求最值的问题 (Using the genetic algorithm matlab implementation and use of the method of continuous functions in general for finding the best value problems)

this ias uase in anay appacation

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.)