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[DataMining] Multistep-Wind-Speed-Forecasting-Based-on-Wavelet DL : 0: Considering the randomness and volatility of wind, a method based on B-spline neural network optimized by particle swarm optimization is proposed to predict the short-term wind speed
Date : 2025-12-27 Size : 1.94mb User : Ephraim Admassu
[DataMining] 24567507qpso-svm DL : 0: 一个粒子群优化的支持向量机，运行过的，很好用，供初学者参考-A particle swarm optimization support vector machine (SVM), running, useful, reference for beginners
Date : 2025-12-27 Size : 13kb User : 李冰
[DataMining] pso DL : 0: 标准群算法的matlab实现函数，带有两个demo-Standard swarm algorithm matlab achieve function with two demo
Date : 2025-12-27 Size : 20kb User : 李凌峰
[DataMining] rgyq DL : 0: 人工鱼群算法的实现代码，有效处理优化寻优问题，比遗传算法、PSO具有更好的优化效果-Artificial fish swarm algorithm code to deal effectively optimize the optimization problem, with better optimization results than the genetic algorithm
Date : 2025-12-27 Size : 2kb User : 陈翔
[DataMining] DPSO DL : 0: 离散粒子群算法。粒子群算法原来只能处理连续变量，算法进行了改进，可以处理离散变量-Discrete particle swarm optimization algorithm, particle swarm optimization algorithm can only deal with continuous variables, the algorithm is improved, can handle discrete variables
Date : 2025-12-27 Size : 3kb User : 张春辉
[DataMining] SVC DL : 0: 建立LibSVM预测模型，基于网格算法、粒子群算法、遗传算法优化了模型参数，并由最终模型预测了给定切削参数下零件的粗糙度等级。-Establish LibSVM prediction model, grid-based algorithm, particle swarm optimization, genetic algorithm to optimize the parameters of the model, the final model prediction given by the cutting parameters of parts roughness class.
Date : 2025-12-27 Size : 635kb User : 墨非
[DataMining] Wind-Speed-Combined-Prediction DL : 0: 针对风电场短期风速的预测提出一种基于小波变换的组合预测方法。首先利用Mallat 算法对短期风速时间序列进行db3 小波三层分解与重构，得到短期风速时间序列的近似分量和细节分量。针对近似分量和细节分量的不同特性，对近似分量利用粒子群算法优化的最小二乘支持向量机进行预测，对细节分量利用自回归求和滑动平均模型进行预测。最后各预测模型预测值组合叠加得到最终的短期风速预测值。仿真结果表明该方法具有较高的预测准确度。-In order to improve short-term wind speed prediction accuracy of wind farms，a combined prediction method based on the wavelet transform is proposed. Firstly，the db3 wavelet is used for three-layer decomposition and reconstruction for short-term wind speed time series through Mallat algorithm. The approximation components and the detail components of the short-term wind speed are then obtained. Next，according to the different characteristics of these components，the least square support vector machine optimized by particle swarm algorithm and the autoregressive integrated moving average model are adopted as the predictivemodels for the approximate components and the detail components respectively. Then，the final predictive value of the short-term wind speed is obtained by the combination of the two components. The simulation results indicate that higher accuracy can be obtained in this prediction method.
Date : 2025-12-27 Size : 283kb User : kiel