Finite State Machines and Recurrent Neural Networks -- Automata and Dynamical Systems Approaches

[1]  Peter Tiňo,et al.  Fixed points in two-neuron discrete time recurrent networks: stability and bifurcation considerations , 1995 .

[2]  Peter Tiňo,et al.  Non-standard topologies of neuron field in self-organizing feature maps , 1995 .

[3]  Panagiotis Manolios,et al.  First-Order Recurrent Neural Networks and Deterministic Finite State Automata , 1994, Neural Computation.

[4]  Liang Jin,et al.  Absolute stability conditions for discrete-time recurrent neural networks , 1994, IEEE Trans. Neural Networks.

[5]  Morris W. Hirsch,et al.  Saturation at high gain in discrete time recurrent networks , 1994, Neural Networks.

[6]  Don R. Hush,et al.  Bounds on the complexity of recurrent neural network implementations of finite state machines , 1993, Neural Networks.

[7]  Michael C. Mozer,et al.  A Unified Gradient-Descent/Clustering Architecture for Finite State Machine Induction , 1993, NIPS.

[8]  Padhraic Smyth,et al.  Learning Finite State Machines With Self-Clustering Recurrent Networks , 1993, Neural Computation.

[9]  Mathukumalli Vidyasagar,et al.  Location and stability of the high-gain equilibria of nonlinear neural networks , 1993, IEEE Trans. Neural Networks.

[10]  Hava T. Siegelmann,et al.  The complexity of language recognition by neural networks , 1992, Neurocomputing.

[11]  Xin Wang,et al.  Discrete-Time versus Continuous-Time Models of Neural Networks , 1992, J. Comput. Syst. Sci..

[12]  Xin Wang,et al.  Stability of fixed points and periodic orbits and bifurcations in analog neural networks , 1992, Neural Networks.

[13]  L. K. Li,et al.  Fixed point analysis for discrete-time recurrent neural networks , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[14]  K. Doya,et al.  Bifurcations in the learning of recurrent neural networks , 1992, [Proceedings] 1992 IEEE International Symposium on Circuits and Systems.

[15]  Raymond L. Watrous,et al.  Induction of Finite-State Languages Using Second-Order Recurrent Networks , 1992, Neural Computation.

[16]  C. Lee Giles,et al.  Learning and Extracting Finite State Automata with Second-Order Recurrent Neural Networks , 1992, Neural Computation.

[17]  Raymond L. Watrous,et al.  Induction of Finite-State Automata Using Second-Order Recurrent Networks , 1991, NIPS.

[18]  C. Lee Giles,et al.  Extracting and Learning an Unknown Grammar with Recurrent Neural Networks , 1991, NIPS.

[19]  Xin Wang,et al.  Period-doublings to chaos in a simple neural network , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[20]  Noga Alon,et al.  Efficient simulation of finite automata by neural nets , 1991, JACM.

[21]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[22]  Michael I. Jordan.  Attractor dynamics and parallelism in a connectionist sequential machine , 1990 .

[23]  James L. McClelland,et al.  Finite State Automata and Simple Recurrent Networks , 1989, Neural Computation.

[24]  Morris W. Hirsch,et al.  Convergent activation dynamics in continuous time networks , 1989, Neural Networks.

[25]  David Bryan,et al.  Combinational profiles of sequential benchmark circuits , 1989, IEEE International Symposium on Circuits and Systems,.

[26]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[27]  Robert C. Minnick,et al.  Linear-Input Logic , 1961, IRE Trans. Electron. Comput..

[28]  Michael H. Freedman,et al.  Computation in discrete-time dynamical systems , 1995 .

[29]  Stefen Hui,et al.  Dynamical analysis of the brain-state-in-a-box (BSB) neural models , 1992, IEEE Trans. Neural Networks.

[30]  Max H. Garzon,et al.  Global Dynamics in Neural Networks II , 1990, Complex Syst..