Hybrid learning algorithm based neural networks for short-term load forecasting
暂无分享,去创建一个
[1] José Neves,et al. The fully informed particle swarm: simpler, maybe better , 2004, IEEE Transactions on Evolutionary Computation.
[2] Jianjun Wang,et al. An annual load forecasting model based on support vector regression with differential evolution algorithm , 2012 .
[3] Chih-Min Lin,et al. Adaptive recurrent cerebellar model articulation controller for linear ultrasonic motor with optimal learning rates , 2007, Neurocomputing.
[4] Saman K. Halgamuge,et al. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.
[5] Wei-Chiang Hong,et al. Electric load forecasting by support vector model , 2009 .
[6] Rahmat-Allah Hooshmand,et al. A hybrid intelligent algorithm based short-term load forecasting approach , 2013 .
[7] Jin-Tsong Jeng,et al. Annealing robust radial basis function networks for function approximation with outliers , 2004, Neurocomputing.
[8] James Kennedy,et al. Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.
[9] S. A. Soliman,et al. Short-term electric load forecasting based on Kalman filtering algorithm with moving window weather and load model , 2004 .
[10] Jianzhou Wang,et al. An adaptive fuzzy combination model based on self-organizing map and support vector regression for electric load forecasting , 2012 .
[11] T. Hesterberg,et al. A regression-based approach to short-term system load forecasting , 1989, Conference Papers Power Industry Computer Application Conference.
[12] Jian Wang,et al. Short, medium and long term load forecasting model and virtual load forecaster based on radial basis function neural networks , 2010 .
[13] Tsung-Ying Sun,et al. Effective Learning Rate Adjustment of Blind Source Separation Based on an Improved Particle Swarm Optimizer , 2008, IEEE Transactions on Evolutionary Computation.
[14] Yiyu Zhou,et al. Approach based on combination of vector neural networks for emitter identification , 2010 .
[15] Carlos E. Pedreira,et al. Neural networks for short-term load forecasting: a review and evaluation , 2001 .
[16] Shyh-Jier Huang,et al. Short-term load forecasting via ARMA model identification including non-Gaussian process considerations , 2003 .
[17] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[18] Zahra Garkani-Nejad,et al. Comparison of conventional artificial neural network and wavelet neural network in modeling the half-wave potential of aldehydes and ketones , 2010 .
[19] H. Mori,et al. Optimal fuzzy inference for short-term load forecasting , 1995 .
[20] Chia-Nan Ko,et al. Time series prediction using RBF neural networks with a nonlinear time-varying evolution PSO algorithm , 2009, Neurocomputing.
[21] Hiroyuki Mori,et al. A preconditioned fast decoupled power flow method for contingency screening , 1995 .
[22] Mohammed E. El-Telbany,et al. Short-term forecasting of Jordanian electricity demand using particle swarm optimization , 2008 .
[23] Chia-Nan Ko,et al. Short-term load forecasting using SVR (support vector regression)-based radial basis function neural network with dual extended Kalman filter , 2013 .
[24] Wei-Chiang Hong,et al. Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artific , 2011 .