-
5
-
-
0030586641
-
The dynamics of discrete-time computation, with application to recurrent neural networks and finite state machine extraction
-
Casey M. The dynamics of discrete-time computation, with application to recurrent neural networks and finite state machine extraction. Neural Computation. 8(6):1996;1135-1178.
-
(1996)
Neural Computation
, vol.8
, Issue.6
, pp. 1135-1178
-
-
Casey, M.1
-
7
-
-
0003130308
-
A unified gradient-descent/clustering architecture for finite state machine induction
-
J. Cowan, G. Tesauro & J. Alspector (Eds.), San Mateo, CA: Morgan Kaufmann
-
Das, S. & Mozer, M. (1994). A unified gradient-descent/clustering architecture for finite state machine induction. In J. Cowan, G. Tesauro & J. Alspector (Eds.), Advances in neural information processing systems 6, pp. 19-26. San Mateo, CA: Morgan Kaufmann.
-
(1994)
Advances in Neural Information Processing Systems
, vol.6
, pp. 19-26
-
-
Das, S.1
Mozer, M.2
-
8
-
-
0003979694
-
Learning the initial state of a second-order recurrent neural network during regular-language inference
-
Forcada M.L., Carrasco R.C. Learning the initial state of a second-order recurrent neural network during regular-language inference. Neural Computation. 7:1995;923-930.
-
(1995)
Neural Computation
, vol.7
, pp. 923-930
-
-
Forcada, M.L.1
Carrasco, R.C.2
-
10
-
-
0029560406
-
Learning a class of large finite state machines with a recurrent neural network
-
Giles C.L., Horne B.G., Lin T. Learning a class of large finite state machines with a recurrent neural network. Neural Networks. 8(9):1995;1359-1365.
-
(1995)
Neural Networks
, vol.8
, Issue.9
, pp. 1359-1365
-
-
Giles, C.L.1
Horne, B.G.2
Lin, T.3
-
11
-
-
0028268852
-
On the principles of fuzzy neural networks
-
Gupta M.N., Rao D.H. On the principles of fuzzy neural networks. Fuzzy Sets and Systems. 61:1994;1-18.
-
(1994)
Fuzzy Sets and Systems
, vol.61
, pp. 1-18
-
-
Gupta, M.N.1
Rao, D.H.2
-
14
-
-
21844516878
-
Recurrent fuzzy logic using neural networks
-
T. Furuhashi (Ed.), Berlin: Springer
-
Khan, E. & Unal, F. (1995). Recurrent fuzzy logic using neural networks. In T. Furuhashi (Ed.), Advances in fuzzy logic, neural networks, and genetic algorithms - lecture notes in artificial intelligence. Berlin: Springer.
-
(1995)
Advances in Fuzzy Logic, Neural Networks, and Genetic Algorithms - Lecture Notes in Artificial Intelligence
-
-
Khan, E.1
Unal, F.2
-
16
-
-
0026995322
-
Random DFAs can be approximately learned from sparse uniform examples
-
D. Haussler (Ed.), New York: ACM Press
-
Lang, K. (1992). Random DFAs can be approximately learned from sparse uniform examples. In D. Haussler (Ed.), Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory (pp. 45-52). New York: ACM Press.
-
(1992)
Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory
, pp. 45-52
-
-
Lang, K.1
-
17
-
-
0344729559
-
Experimental comparison of the effect of order in recurrent neural networks
-
I. Guyon, and P.S.P. Wang (Eds.), Singapore: World Scientific
-
Miller, S. & Giles, C.L. (1993). Experimental comparison of the effect of order in recurrent neural networks. In I. Guyon, and P.S.P. Wang (Eds.), Advances in pattern recognition systems using neural network technologies (pp. 205-228). Singapore: World Scientific.
-
(1993)
Advances in Pattern Recognition Systems Using Neural Network Technologies
, pp. 205-228
-
-
Miller, S.1
Giles, C.L.2
-
18
-
-
0028192731
-
Logical operation based fuzzy MLP for classification and rule generation
-
Mitra S., Pal S.K. Logical operation based fuzzy MLP for classification and rule generation. Neural Networks. 7(2):1994;353-373.
-
(1994)
Neural Networks
, vol.7
, Issue.2
, pp. 353-373
-
-
Mitra, S.1
Pal, S.K.2
-
19
-
-
0029880174
-
Extraction of rules from discrete-time recurrent neural network
-
Omlin C.W., Giles C.L. Extraction of rules from discrete-time recurrent neural network. Neural Networks. 9(1):1995;41-52.
-
(1995)
Neural Networks
, vol.9
, Issue.1
, pp. 41-52
-
-
Omlin, C.W.1
Giles, C.L.2
-
20
-
-
0030286473
-
Constructing deterministic finite-state automata in recurrent neural networks
-
Omlin C.W., Giles C.L. Constructing deterministic finite-state automata in recurrent neural networks. Journal of the ACM. 43(6):1996;937-972.
-
(1996)
Journal of the ACM
, vol.43
, Issue.6
, pp. 937-972
-
-
Omlin, C.W.1
Giles, C.L.2
-
21
-
-
0029747022
-
Representation of fuzzy finite state automata in continuous recurrent neural networks
-
B.J. Shen (Ed.), Piscataway, NJ: IEEE Press
-
Omlin, C.W., Thornber, K.K. & Giles, C.L. (1996). Representation of fuzzy finite state automata in continuous recurrent neural networks. In B.J. Shen (Ed.), Proceedings of the IEEE International Conference on Neural networks (pp. 1023-1028). Piscataway, NJ: IEEE Press.
-
(1996)
Proceedings of the IEEE International Conference on Neural Networks
, pp. 1023-1028
-
-
Omlin, C.W.1
Thornber, K.K.2
Giles, C.L.3
-
22
-
-
0031996475
-
Fuzzy finite-state automata can be deterministically encoded into recurrent neural networks
-
to be published
-
Omlin C.W., Thornber K.K., & Giles C.L. (1998). Fuzzy finite-state automata can be deterministically encoded into recurrent neural networks. IEEE Transactions on Fuzzy Systems (to be published).
-
(1998)
IEEE Transactions on Fuzzy Systems
-
-
Omlin, C.W.1
Thornber, K.K.2
Giles, C.L.3
-
23
-
-
0001202597
-
Learning state space trajectories in recurrent neural networks
-
Pearlmutter B.A. Learning state space trajectories in recurrent neural networks. Neural Computation. 1:1989;263-269.
-
(1989)
Neural Computation
, vol.1
, pp. 263-269
-
-
Pearlmutter, B.A.1
-
24
-
-
0027593387
-
Fuzzy neural networks and neurocomputations
-
Pedrycz W. Fuzzy neural networks and neurocomputations. Fuzzy Sets and Systems. 56:1993;1-28.
-
(1993)
Fuzzy Sets and Systems
, vol.56
, pp. 1-28
-
-
Pedrycz, W.1
-
25
-
-
0000646059
-
Learning internal representations by error propagation
-
D.E. Rumelhart & J.L. McClelland (Eds.), Foundations, Cambridge, MA: MIT Press
-
Rumelhart, D.E., Hinton, G.E. & Williams, R.J. (1986). Learning internal representations by error propagation. In D.E. Rumelhart & J.L. McClelland (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Vol. 1: Foundations, pp. 318-362. Cambridge, MA: MIT Press.
-
(1986)
Parallel Distributed Processing: Explorations in the Microstructure of Cognition
, vol.1
, pp. 318-362
-
-
Rumelhart, D.E.1
Hinton, G.E.2
Williams, R.J.3
-
26
-
-
0026927202
-
Fuzzy min-max neural networks - Part 1: Classifications
-
Simpson P.K. Fuzzy min-max neural networks - Part 1: classifications. IEEE Transactions on Neural Networks. 3(5):1992;776-786.
-
(1992)
IEEE Transactions on Neural Networks
, vol.3
, Issue.5
, pp. 776-786
-
-
Simpson, P.K.1
-
27
-
-
0027542561
-
Fuzzy min-max neural networks - Part 2: Clustering
-
Simpson P.K. Fuzzy min-max neural networks - Part 2: clustering. IEEE Transactions on Neural Networks. 4(1):1993;32-45.
-
(1993)
IEEE Transactions on Neural Networks
, vol.4
, Issue.1
, pp. 32-45
-
-
Simpson, P.K.1
-
28
-
-
0344729558
-
An effective learning method for max-min neural networks
-
M.E. Pollack (Ed.), Denver, CO: Professional Book Center
-
Teow, L. & Loe, K. (1997). An effective learning method for max-min neural networks. In M.E. Pollack (Ed.), Proceedings of the 15th International Joint Conference on Artificial Intelligence (IJCAI-97), Vol. 2, pp. 1134-1139. Denver, CO: Professional Book Center.
-
(1997)
Proceedings of the 15th International Joint Conference on Artificial Intelligence (IJCAI-97)
, vol.2
, pp. 1134-1139
-
-
Teow, L.1
Loe, K.2
-
29
-
-
0001202594
-
A learning algorithm for continually running fully recurrent neural networks
-
Williams R., Zipser D. A learning algorithm for continually running fully recurrent neural networks. Neural Computation. 1:1989;270-280.
-
(1989)
Neural Computation
, vol.1
, pp. 270-280
-
-
Williams, R.1
Zipser, D.2
-
30
-
-
0000003489
-
Learning finite state machines with self-clustering recurrent networks
-
Zeng Z., Goodman R.M., Smyth P. Learning finite state machines with self-clustering recurrent networks. Neural Computation. 5:1993;976-990.
-
(1993)
Neural Computation
, vol.5
, pp. 976-990
-
-
Zeng, Z.1
Goodman, R.M.2
Smyth, P.3
|