Dynamical systems produced by recurrent neural networks

[1]  Jacob Barhen,et al.  Learning a trajectory using adjoint functions and teacher forcing , 1992, Neural Networks.

[2]  Masa-aki Sato,et al.  APOLONN brings us to the real world: learning nonlinear dynamics and fluctuations in nature , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[3]  Barak A. Pearlmutter.  Learning State Space Trajectories in Recurrent Neural Networks , 1989, Neural Computation.

[4]  Marco Saerens,et al.  Classification of temporal trajectories by continuous-time recurrent nets , 1994, Neural Networks.

[5]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[6]  Josef Hofbauer,et al.  The theory of evolution and dynamical systems , 1988 .

[7]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[8]  Pineda,et al.  Generalization of back-propagation to recurrent neural networks. , 1987, Physical review letters.

[9]  M. Hirsch,et al.  Differential Equations, Dynamical Systems, and Linear Algebra , 1974 .

[10]  Randall D. Beer,et al.  On the Dynamics of Small Continuous-Time Recurrent Neural Networks , 1995, Adapt. Behav..

[11]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[12]  Yuichi Nakamura,et al.  Approximation of dynamical systems by continuous time recurrent neural networks , 1993, Neural Networks.

[13]  Marco Saerens,et al.  Hopfield net generation, encoding and classification of temporal trajectories , 1994, IEEE Trans. Neural Networks.

[14]  Kenji Doya,et al.  Adaptive neural oscillator using continuous-time back-propagation learning , 1989, Neural Networks.

[15]  R. Palmer,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[16]  Guo-Zheng Sun,et al.  Green's Function Method for Fast On-Line Learning Algorithm of Recurrent Neural Networks , 1991, NIPS.

[17]  Pierre Baldi,et al.  Gradient descent learning algorithm overview: a general dynamical systems perspective , 1995, IEEE Trans. Neural Networks.