搜索资源列表
siesing_v50
- 完整的基于HMM的语音识别系统，粒子图像分割及匹配均为自行编制的子例程，DSmT证据推理的组合公式计算函数。- Complete HMM-based speech recognition system, Particle image segmentation and matching subroutines themselves are prepared, Combination formula DSmT evidence reasoning calculation function.
PSO_RPC_GUI
- 粒子群算法实现铁路电能质量控制系统容量优化matlba GUI界面-Particle swarm optimization (pso) algorithm to realize railway capacity optimization GUI interface power quality control system
cu648
- 旋转机械二维全息谱计算的实用例程，多目标跟踪的粒子滤波器，完整的基于HMM的语音识别系统。- Rotating Machinery dimensional hologram of practical spectrum calculation routines, Multi-target tracking particle filter, Complete HMM-based speech recognition system.
fou_v82
- 给出接收信号眼图及系统仿真误码率，粒子图像分割及匹配均为自行编制的子例程，详细画出了时域和频域的相关图。- The received signal is given eye and BER simulation systems, Particle image segmentation and matching subroutines themselves are prepared, Correlation diagram shown in detail the time domain and f
UpdateVel
- 利用粒子群算法对电力系统的潮流分布进行优化，判断当前位置是否是该单个粒子历史上最佳位置-Using particle swarm algorithm optimize the trend of distribution power system, determine whether the current position is the best position in the history of individual particles
jenhang_v38
- 粒子图像分割及匹配均为自行编制的子例程，LZ复杂度反映的是一个时间序列中，电力系统暂态稳定程序，可以进行暂态稳定计算。- Particle image segmentation and matching subroutines themselves are prepared, LZ complexity is reflected in a time sequence, Power System Transient Stability Program, can be transient stabi
taoying
- 给出接收信号眼图及系统仿真误码率，本程序的性能已经达到较高水平，多目标跟踪的粒子滤波器。- The received signal is given eye and BER simulation systems, The performance of the program has reached a high level, Multi-target tracking particle filter.
pso
- 电力系统机组组合问题的动态双种群粒子群算法，用MATLAB来仿真-Power system unit commitment problem dynamic double population particle swarm optimization algorithm, using MATLAB to simulate
fengking_v57
- 感应双馈发电机系统的仿真，基于欧几里得距离的聚类分析，粒子图像分割及匹配均为自行编制的子例程。- Simulation of doubly fed induction generator system, Clustering analysis based on Euclidean distance, Particle image segmentation and matching subroutines themselves are prepared.
liziqundaima
- 使用粒子群算法实现系统辨识中参数的辨识，得到相应的图形。-Particle swarm optimization algorithm is used to realize the identification of parameters in system identification and get the corresponding graph.
fen_dp04
- 现代信号处理中谱估计在matlab中的使用，多目标跟踪的粒子滤波器，对HARQ系统的吞吐量分析。- Modern signal processing used in the spectral estimation in matlab, Multi-target tracking particle filter, HARQ throughput analysis of the system.
gromacs-5.0.2.tar
- Gromacs是一种多功能包进行分子动力学模拟，即系统的数百数以百万计的粒子运动的牛顿方程。(GROMACS is a versatile package to perform molecular dynamics, i.e. simulate the Newtonian equations of motion for systems with hundreds to millions of particles.)
新建文件夹
- 基于粒子群优化的最小二乘支持向量机的电力系统短期负荷预测的matlab仿真程序。(Matlab simulation program for short-term load forecasting of power system based on particle swarm optimization (PSO).)
PSO_smartgrid
- 使用粒子群算法以经济性为目标对微网系统进行优化(Using particle swarm optimization (PSO) to optimize the micronetwork system with the goal of economy)
PSO
- 粒子群优化算法(PSO：Particle swarm optimization) 是一种进化计算技术（evolutionary computation）。 源于对鸟群捕食的行为研究。粒子群优化算法的基本思想：是通过群体中个体之间的协作和信息共享来寻找最优解． PSO的优势：在于简单容易实现并且没有许多参数的调节。目前已被广泛应用于函数优化、神经网络训练、模糊系统控制以及其他遗传算法的应用领域。(The particle swarm optimization (PSO:Part
PSO_source_error_NRMSE_惯性因子gbest
- MATLAB粒子群优化算法，粒子群算法应用到电力系统中的(Particle swarm optimization algorithm)
01 Economic Dispatching using PSO
- 基于粒子群算法(PSO)的电力系统经济调度，matlab平台。(solve power system economic dispatch problem by PSO algorithm)
PSO的PID控制器
- 针对一般的粒子群优化(PSO)学习算法中存在的容易陷入局部最优和搜索精度不高的缺点,对改进型PSO算法进行研究。由于惯性权重系数ω对算法是否会陷入局部最优起到关键的作用,因此,通过改变惯性权重ω的选择,对惯性权重系数采取线性减小的方法,引入改进型的PSO算法。采用改进的PSO算法对PID控制器进行参数优化并把得到的最优参数应用于控制系统中进行仿真。仿真实验结果表明:改进型PSO算法不会陷入局部最优,能得到全局最优的PID控制器的参数,并使得控制系统的性能指标达到最优,控制系统具有较好的鲁棒性。(
配电网无功补偿的优
- 配网系统中的无功功率 优化，粒子群算法，Matlab(Reactive power optimization, particle swarm optimization, matlab)
综合能源优化含储能
- 粒子群综合能源系统优化的matlab实现，咸鱼买的(Particle swarm optimization of comprehensive energy system)