Echo State Networks and Extreme Learning Machines: A Comparative Study on Seasonal Streamflow Series Prediction
暂无分享,去创建一个
[1] Erkki Oja,et al. Independent Component Analysis , 2001 .
[2] Simon Haykin,et al. Neural Networks: A Comprehensive Foundation , 1998 .
[3] José Carlos Príncipe,et al. Water Inflow Forecasting using the Echo State Network: a Brazilian Case Study , 2007, 2007 International Joint Conference on Neural Networks.
[4] Herbert Jaeger,et al. The''echo state''approach to analysing and training recurrent neural networks , 2001 .
[5] Hugo Valadares Siqueira,et al. Echo State Networks for Seasonal Streamflow Series Forecasting , 2012, IDEAL.
[6] Chee Kheong Siew,et al. Extreme learning machine: Theory and applications , 2006, Neurocomputing.
[7] Rosangela Ballini,et al. Top-down strategies based on adaptive fuzzy rule-based systems for daily time series forecasting , 2011 .
[8] Fernando José Von Zuben,et al. An extended echo state network using Volterra filtering and principal component analysis , 2012, Neural Networks.
[9] Fernando José Von Zuben,et al. An echo state network architecture based on volterra filtering and PCA with application to the channel equalization problem , 2011, The 2011 International Joint Conference on Neural Networks.
[10] Fernando José Von Zuben,et al. Improved second-order training algorithms for globally and partially recurrent neural networks , 1999, IJCNN.
[11] Fernando J. Von Zuben,et al. Efficient Second-Order Learning Algorithms for Discrete-Time Recurrent Neural Networks , 1999 .
[12] J. Nazuno. Haykin, Simon. Neural networks: A comprehensive foundation, Prentice Hall, Inc. Segunda Edición, 1999 , 2000 .
[13] José Carlos Príncipe,et al. Analysis and Design of Echo State Networks , 2007, Neural Computation.
[14] Herbert Jaeger,et al. Reservoir computing approaches to recurrent neural network training , 2009, Comput. Sci. Rev..
[15] 俊一 甘利,et al. A. Hyvärinen, J. Karhunen and E. Oja, Independent Component Analysis, Jhon Wiley & Sons, 2001年,504ページ. (根本幾・川勝真喜訳:独立成分分析——信号解析の新しい世界,東京電機大学出版局,2005年,532ページ.) , 2010 .
[16] S. Haykin,et al. Adaptive Filter Theory , 1986 .
[17] Chee Kheong Siew,et al. Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.