Optimization and applications of echo state networks with leaky- integrator neurons
暂无分享,去创建一个
Herbert Jaeger | Mantas Lukosevicius | Dan Popovici | Udo Siewert | D. Popovici | H. Jaeger | M. Lukoševičius | U. Siewert
[1] Jürgen Schmidhuber,et al. Training Recurrent Networks by Evolino , 2007, Neural Computation.
[2] Eduardo D. Sontag,et al. Computational Aspects of Feedback in Neural Circuits , 2006, PLoS Comput. Biol..
[3] José Carlos Príncipe,et al. Analysis and Design of Echo State Networks , 2007, Neural Computation.
[4] Mantas Lukoševičius,et al. Time Warping Invariant Echo State Networks , 2006 .
[5] Peter Michael Young,et al. A tighter bound for the echo state property , 2006, IEEE Transactions on Neural Networks.
[6] Dean V Buonomano,et al. A learning rule for the emergence of stable dynamics and timing in recurrent networks. , 2005, Journal of neurophysiology.
[7] M. C. Ozturk,et al. Analysis and Design of Echo State Networks for Function Approximation , 2005 .
[8] Jochen J. Steil,et al. Analyzing the weight dynamics of recurrent learning algorithms , 2005, Neurocomputing.
[9] Herbert Jaeger,et al. A tutorial on training recurrent neural networks , covering BPPT , RTRL , EKF and the " echo state network " approach - Semantic Scholar , 2005 .
[10] Marc Strickert,et al. Self-organizing neural networks for sequence processing , 2005 .
[11] Tijn van der Zant,et al. Finding good Echo State Networks to control an underwater robot using evolutionary computations , 2004 .
[12] D. Buonomano,et al. The neural basis of temporal processing. , 2004, Annual review of neuroscience.
[13] Harald Haas,et al. Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication , 2004, Science.
[14] Eric R. Ziegel,et al. The Elements of Statistical Learning , 2003, Technometrics.
[15] M. Hinder,et al. The Case for an Internal Dynamics Model versus Equilibrium Point Control in Human Movement , 2003, The Journal of physiology.
[16] Malur K. Sundareshan,et al. Trajectory generation and modulation using dynamic neural networks , 2003, IEEE Trans. Neural Networks.
[17] Robert P. W. Duin,et al. The combining classifier: to train or not to train? , 2002, Object recognition supported by user interaction for service robots.
[18] Henry Markram,et al. Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations , 2002, Neural Computation.
[19] Herbert Jaeger,et al. Adaptive Nonlinear System Identification with Echo State Networks , 2002, NIPS.
[20] David Barber,et al. Dynamic Bayesian Networks with Deterministic Latent Tables , 2002, NIPS.
[21] Pierre Geurts,et al. Pattern Extraction for Time Series Classification , 2001, PKDD.
[22] Herbert Jaeger,et al. The''echo state''approach to analysing and training recurrent neural networks , 2001 .
[23] John F. Kolen,et al. Gradient Calculations for Dynamic Recurrent Neural Networks , 2001 .
[24] Mineichi Kudo,et al. Multidimensional curve classification using passing-through regions , 1999, Pattern Recognit. Lett..
[25] G B Stanley,et al. Reconstruction of Natural Scenes from Ensemble Responses in the Lateral Geniculate Nucleus , 1999, The Journal of Neuroscience.
[26] B. Farhang-Boroujeny,et al. Adaptive Filters: Theory and Applications , 1999 .
[27] Josef Kittler,et al. Combining classifiers , 1996, Proceedings of 13th International Conference on Pattern Recognition.
[28] Barak A. Pearlmutter. Gradient calculations for dynamic recurrent neural networks: a survey , 1995, IEEE Trans. Neural Networks.
[29] Guo-Zheng Sun,et al. Time Warping Invariant Neural Networks , 1992, NIPS.
[30] Roberto Pieraccini,et al. Time-Warping Network: A Hybrid Framework for Speech Recognition , 1991, NIPS.
[31] Lawrence R. Rabiner,et al. A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.
[32] F. Itakura,et al. Minimum prediction residual principle applied to speech recognition , 1975 .