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基于信道的历史信息来预测不同信道的未来可用时间可以帮助CR选择一个最佳信道进行信息传递。不同的预测准则应用到周期或是随机的开关模式中。一个CR可以学习以往不同信道的模式。我们提出一个简单的分类和学习方法去检测模式的类型和收集需要的信息用于智能信道的选择。MATLAB仿真结果显示提出的方案在随机信道的随机和周期模式中都有优越的表现。随着时间的变化，新到的切换次数减少到55 ，并且延迟时间也相应减少，而吞吐量却在提高。(Prediction of future availability times of different channels based on history information helps a cognitive radio (CR) to select the best channels for control and data transmission. Different prediction rules apply to periodic and stochastic ON-OFF patterns. A CR can learn the patterns in different channels over time. We propose a simple classification and learning method to detect the pattern type and to gather the needed information for intelligent channel selection. Matlab simulations show that the proposed method outperforms opportunistic random channel selection both with stochastic and periodic channel patterns. The amount of channel switches needed over time reduces up to 55 , which reduces also the delay and increases the throughput.)

说明：  首先对电网节点优化，再PQ分解法求解系统潮流。(First of all, optimization of the power grid nodes, then PQ decomposition method for calculating the trend.)

超宽带5阶高斯脉冲发射波形经BPSK调制后系统性能(5-order Gaussian pulse UWB emission waveform modulated by the BPSK system performance)

空时编码技术的matlab仿真，供通信仿真学习适用。(Space-time coding matlab simulation technology for communication simulation study apply.)

说明：  低秩表示法对子空间结构的鲁棒恢复。图像复原低秩表示算法。(Robust Recovery of Subspace Structures by Low-Rank Representation)

ARMA模型基于时间序列分析和预测，ARMA matlab程序源代码(ARMA model based on time series analysis and forecasting, ARMA program matlab source code)

estimate the spectrum

说明：  matlab源代码，是重要的地震勘探源代码，工具箱程序(matlab source code, it is important to seismic exploration source code, toolbox procedures)

GP算法计算关联维的 Matlab 程序 GP算法计算关联维的 Matlab 程序(GP algorithm for calculating the correlation dimension of the Matlab program GP correlation dimension algorithm of Matlab procedures)

城市交通流的运行存在着高度的复杂性、时变性和随机性,实时准确的交通流预测是智能交通系统,特别是先进的交通管理系统与先进的出行者信息系统研究的关键. 基于交通流预测的特点,给出了基于遗传算法的小波神经 网络的交通预测模型GA-WNN ,用具有自然进化规律的遗传算法来对小波神经网络的连接权值和伸缩平移尺度进行前期优化训练,部分代替了小波框架神经网络中按单一梯度方向进行参数优化的梯度下降法,克服了单一梯度下降法易陷入局部极小和引起振荡效应等缺陷. 仿真实验验证了GA-WNN 预测模型对短时交通流的预测的有效性.(For the high complexity ,time-variation and probability of urban traffic flow , its real-time and exact prediction is critical to the research of intelligent traffic systems , especially for the advanced traffic manage-ment system and advanced traveler information system. Based on the character of the traffic flow prediction , a GA-WNN model is given based on the wavelet neural network with genetic algorithm. The genetic algorithm of natural evolving law for the gradient descendent algorithm in Wavelet Neural Network is partly substituted to pre-optimize the connection weight and the extension scale of the wavelet neural network and later optimize the parameters along a single gradient vector. This method overcomes some drawbacks when there exists a single gradient descendent algorithm , such as local minimum and oscillation. A short-time traffic flow prediction sim- ulation using the GA2WNN prediction model demonstrates the validity of the model .)