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Comparison of two new intelligent wind speed forecasting approaches based on Wavelet Packet Decomposition, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Artificial Neural Networks 期刊论文
Energy Conversion and Management, 2018, 卷号: 155, 页码: 188-200
作者:  Liu, Hui;  Mi, Xiwei;  Li, Yanfei*
收藏  |  浏览/下载：41/0  |  提交时间：2019/12/03
Wind speed forecasting  Wavelet packet decomposition  Complete Ensemble Empirical Mode Decomposition with Adaptive Noise  Artificial Neural Network  ANN  Artificial Neural Network  AR  Auto Regressive  ARIMA  Auto Regressive Integrated Moving Average  BA  Bat Algorithm  BP  Back-propagation Neural Network  CEEMDAN  Complete Ensemble Empirical Mode Decomposition  CG  Conjugate Gradient  CLSFPA  Flower Pollination Algorithm with Chaotic Local Search  CNN  Convolutional Neural Network  CS  Compressive Sensing  CSA  Cuckoo Search Algorithm  EEMD  Ensemble Empirical Mode Decomposition  ESM  Exponential Smoothing Method  FA  Firefly Algorithm  FAC  First-order Adaptive Coefficient  GA  Genetic Algorithm  GRNN  General Regression Neural Network  HBSA  Hybrid Backtracking Search Algorithm  HWM  Holt-Winters Model  IMFs  Intrinsic Mode Functions  IS  Input parameter Selection  KF  Kalman filter  LSSVM  Least Square Support Vector Machine  MAE  Mean Absolute Error  MAPE  Mean Absolute Percentage Error  MLP  Multilayer Perceptron Neural Network  MOBA  Multi Objective Bat Algorithm  NNCT  No Negative Constraint Theory  NSGA  Non-dominated Sorting Genetic Algorithm  OVMD  Optimized Variational Mode Decomposition  PSO  Particle Swam Optimization  PSOSA  Particle Swarm Optimization based on Simulated Annealing  PSR  Phase Space Reconstruction  RBF  Radial Basis Function Neural Network  RMSE  Root Mean Square Error  SAC  Second-order Adaptive Coefficient  SAM  Seasonal Adjustment Method  SDA  Secondary Decomposition Algorithm  SEA  Seasonal Exponential Adjustment  SOM  Self-Organizing feature Maps  SSA  Singular Spectrum Analysis  SVR  Support Vector Regression  VMD  Variational Mode Decomposition  v-SVM  v-Support Vector Machine  WD  Wavelet Decomposition  WPD  Wavelet Packet Decomposition  
A novel fault diagnosis method based on optimal relevance vector machine 期刊论文
Neurocomputing, 2017, 卷号: 267, 页码: 651-663
作者:  He, Shiming;  Xiao, Long;  Wang, Yalin;  Liu, Xinggao*;  Yang, Chunhua
收藏  |  浏览/下载：10/0  |  提交时间：2019/12/03
Application of multi-objective controller to optimal tuning of PID gains for a hydraulic turbine regulating system using adaptive grid particle swam optimization 期刊论文
ISA Transactions, 2015, 卷号: 56, 期号: Volume 56, 页码: 173-187
作者:  Chen, Zhihuan;  Yuan, Yanbin;  Yuan, Xiaohui*;  Huang, Yuehua;  Li, Xianshan
收藏  |  浏览/下载：10/0  |  提交时间：2019/12/04
坐卧式外骨骼下肢康复机器人的运动训练策略和交互控制方法 学位论文
工学博士, 中国科学院自动化研究所: 中国科学院大学, 2014
作者:  胡进
收藏  |  浏览/下载：208/0  |  提交时间：2015/09/02
Divided range multi-objective particle swam optimization algorithm in economic management optimization 期刊论文
International Journal of Applied Mathematics and Statistics, 2013, 卷号: 51, 期号: 23, 页码: 70-78
作者:  Zheng, Hao;  Zhao, Xiang
收藏  |  浏览/下载：1/0  |  提交时间：2019/12/23
Classification of hyperspectral image based on SVM optimized by a new particle swarm optimization (EI CONFERENCE) 会议论文
2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2012, June 1, 2012 - June 3, 2012, Nanjing, China
Gao X.; Yu P.; Mao W.; Peng D.
收藏  |  浏览/下载：15/0  |  提交时间：2013/03/25
Support Vector Machine (SVM) is used to classify hyperspectral remote sensing image in this paper. Radial Basis Function (RBF)  which is most widely used  is chosen as the kernel function of SVM. Selection of kernel function parameter is a pivotal factor which influences the performance of SVM. For this reason  Particle Swarm Optimization (PSO) is provided to get a better result. In order to improve the optimization efficiency of kernel function parameter  firstly larger steps of grid search method is used to find the appropriate rang of parameter. Since the PSO tends to be trapped into local optimal solutions  a weight and mutation particle swam optimization algorithm was proposed  in which the weight dynamically changes with a liner rule and the global best particle mutates per iteration to optimize the parameters of RBF-SVM. At last  a 220-bands hyperspectral remote sensing image of AVIRIS is taken as an experiment  which demonstrates that the method this paper proposed is an effective way to search the SVM parameters and is available in improving the performance of SVM classifiers. 2012 IEEE.  
A Novel Compression Algorithm for Spatiotemporal Data Based on PSO and GA 会议论文
International Conference on Computational Intelligence and Software Engineering (CiSE 2012), Wuhan, China, December 14-16, 2012
作者:  Wu JW(吴俊伟);  Zhu YL(朱云龙);  Ku T(库涛);  Wang L(王亮)
收藏  |  浏览/下载：22/0  |  提交时间：2013/12/26
A Hybrid Particle Swarm Optimization Algorithm for Order Planning Problems of Steel Factory 会议论文
作者:  Zhang, Tao;  Shao, Zhifang;  Zhang, Yuejie;  Yu, Zhiwang;  Jiang, Jianlin
收藏  |  浏览/下载：4/0  |  提交时间：2019/08/22
Remote sensing image fusion using particle swam optimization 会议论文
2010 International Conference on Intelligent Control and Information Processing, ICICIP 2010, Dalian, 2010-01-01
作者:  Han M.;  Yao W.
收藏  |  浏览/下载：2/0  |  提交时间：2019/12/24
Remote sensing image fusion using particle swam optimization 期刊论文
Proceedings of 2010 International Conference on Intelligent Control and Information Processing, ICICIP 2010, 2010, 期号: PART 2
作者:  Han, Min;  Yao, Wei
收藏  |  浏览/下载：3/0  |  提交时间：2019/12/05

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