Description: 优秀论文及配套源码。首先阐述了负荷预测的应用研究现状，概括了负荷预测的特点及其影响因素，归纳了短期负荷预测的常用方法，并分析了各种方法的优劣；接着介绍了作为支持向量机（SVM）理论基础的统计学习理论和SVM的原理，推导了SVM回归模型；本文采用最小二乘支持向量机（LSSVM）模型，根据浙江台州某地区的历史负荷数据和气象数据，分析影响预测的各种因素，总结了负荷变化的规律性，对历史负荷数据中的“异常数据”进行修正，对负荷预测中要考虑的相关因素进行了归一化处理。LSSVM中的两个参数对模型有很大影响，而目前依然是基于经验的办法解决。对此，本文采用粒子群优化算法对模型参数进行寻优，以测试集误差作为判决依据，实现模型参数的优化选择，使得预测精度有所提高。实际算例表明，本文的预测方法收敛性好、有较高的预测精度和较快的训练速度。-first expounds the recent application research of load forecasting, summarized the characteristics of load forecasting and influencing factors, summed up common methods of short-term load forecasting, and analyzed the advantages and disadvantages of each method then introduced statistical learning theory and the principle of SVM as the basis of support vector machine (SVM ) theory, SVM regression model is derived this paper adopted least squares support vector machine (LSSVM) model, according to the historical load data and meteorological data of a certain area of Zhejiang Taizhou, Analysised the various factors affecting the forecast, summed up the regularity of load change , amended "outliers" in the historical load data,the load forecasting factors to be considered were normalized. The two parameters of LSSVM have a significant impact on the model, but it is still soluted based on the experience currently. So, this paper adopted particle swarm optimization algorithm to optimized Platform: |
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Author:NBB |
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Description: 该算法参照《粒子群优化算法机器工程应用》,刘波著,电子工业出版社,算法简单具有粒子群算法的基本思想，是入门学习的好帮手。-The algorithm reference particle swarm optimization algorithm machine engineering applications " , Bo was, Publishing House of Electronics Industry, the algorithm is simple basic idea of the particle swarm algorithm, learning a good helper for entry. Platform: |
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Author:朱叶风 |
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Description: 机器视觉应用于各个领域。当前学术界和工业界在目标跟踪方面开展了大量工作，重点研究的算法有MeanShift跟踪算法、基于在线的booting的跟踪算法、基于粒子群优化的跟踪算法和基于模板匹配的跟踪算法等等。对于这些算法的研究已经取得了一定的成果，但是随着视觉跟踪应用的扩大，其跟踪效果已经不能满足需求，一次当前有研究出了新的算法以满足发展需要，其中具有代表性的跟踪算法就是TLD(Tracking Learning Detector)目标跟踪算法。-Machine vision applied to various fields. Current academia and industry in target tracking undertaken considerable work, focusing on the algorithm has MeanShift tracking algorithm, based on the booting online tracking algorithm based on particle swarm optimization algorithm and tracking tracking algorithm based on template matching and so on. For these algorithms studies have yielded some results, but with the expansion of visual tracking application, tracking effect has been unable to meet its needs, once the current has developed a new algorithm to meet the development needs, including a representative of the tracking algorithm is the TLD (Tracking Learning Detector) target tracking algorithms. Platform: |
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Author:李娜 |
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Home \ Multiobjective Optimization \ Multi-Objective PSO in MATLAB
ypea121-mopso
Multi-Objective PSO in MATLAB
in Multiobjective Optimization 8 Comments 2,599 Views
Multi-Objective Particle Swarm Optimization (MOPSO) is proposed by Coello Coello et al., in 2004. It is a multi-objective version of PSO which incorporates the Pareto Envelope and grid making technique.-
Home
Metaheuristics
Machine Learning
Multiobjective Optimization
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Applications
Home \ Multiobjective Optimization \ Multi-Objective PSO in MATLAB
ypea121-mopso
Multi-Objective PSO in MATLAB
in Multiobjective Optimization 8 Comments 2,599 Views
Multi-Objective Particle Swarm Optimization (MOPSO) is proposed by Coello Coello et al., in 2004. It is a multi-objective version of PSO which incorporates the Pareto Envelope and grid making technique. Platform: |
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Author:ankita |
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Description: 粒子群优化的超限学习机，运行快，拟合效果好，很方便使用(The particle swarm optimization of the overrun learning machine, fast operation, good fitting effect, very convenient to use.) Platform: |
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Author:fengzhong11 |
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Description: 利用主成分分析法结合粒子群（PSO）优化极限学习机(ELM)进行工程费用估计预测(In this paper, principal component analysis (PCA) combined with particle swarm optimization (PSO) optimization extreme learning machine (ELM) is used to estimate and forecast engineering cost) Platform: |
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Author:sunshine ye |
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