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Schmidhuber J, Wierstra D, Gagliolo M, Gomez F (2007) Training recurrent networks by Evolino. Neural Comput 19:757-79 [PubMed]

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Gers FA, Schmidhuber E (2001) LSTM recurrent networks learn simple context-free and context-sensitive languages. IEEE Trans Neural Netw 12:1333-40 [Journal] [PubMed]
Gers FA, Schmidhuber J, Cummins F (2000) Learning to forget: continual prediction with LSTM. Neural Comput 12:2451-71 [PubMed]
Gers FA, Schraudolph N, Schmidhuber J (2002) Learning precise timing with LSTM recurrent networks J Mach Learn Res 3:115-143
Gomez F, Miikkulainen R (1999) Solving non-Markovian control tasks with neuroevolution Proc 16th Intl Joint Conf Artif Intel
Gomez F, Schmidhuber J (2005) Evolving modular fast-weight networks for control Proc 15th Intl Conf Artif Neural Networks :383-389
Gomez FJ (2003) Robust nonlinear control through neuroevolution Unpublished doctoral dissertation, University of Texas at Austin
Gomez FJ, Schmidhuber J (2005) Co-evolving recurrent neurons learn deep memory POMDPS Proc Genetic Evolutionary Computation Conference
Graves A, Schmidhuber J (2005) Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw 18:602-10 [Journal] [PubMed]
Hochreiter S (1991) Untersuchungen zu dynamischen neuronalen Netzen Diplomathesis, Technische Universitat Munchen
Hochreiter S, Bengio Y, Frasconi P, Schmidhuber J (2001) Gradient flow in recurrent nets: The difficulty of learning long-term dependencies A field guide to dynamical recurrent neural networks, Kremer SC:Kolen JF, ed.
Hochreiter S, Schmidhuber J (1997) LSTM can solve hard long time lag problems Advances in neural information processing systems, Mozer MC:Jordan MI:Petsche T, ed. pp.473
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735-80 [PubMed]
Holland JH (1975) Adapatation in natural and artificial systems
Ishii K, van_der_Zant T, Beacanovic V, Ploger PG (2004) Identification ofmotion with echo state network Proc IEEE Oceans :1205-1230
Jaeger H, Haas H (2004) Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304:78-80 [Journal] [PubMed]
Jaeger W (2004) The echo state approach to recurrent neural networks Available onlineat
Maass W (2002) A fresh look at real-time computation in generic recurrent neural circuits Tech Rep Graz:Institute for Theoretical Computer Science
Mackey MC, Glass L (1977) Oscillation and chaos in physiological control systems. Science 197:287-9 [PubMed]
Maillard EP, Gueriot D (1997) RBF neural network, basis functions and genetic algorithms IEEE Intl Conf Neural Networks :2187-2190
Miglino O, Lund HH, Nolfi S (1995) Evolving mobile robots in simulated and real environments. Artif Life 2:417-34 [PubMed]
Miller G, Todd P, Hedge S (1989) Designing neural networks using genetic algorithms Proc 3rd Intl Conf Genetic Algorithms :379-384
Moriarty DE (1997) Symbiotic evolution of neural networks in sequential decision tasks PhD Thesis Department of Computer Science, University of Texas at Austin
Moriarty DE, Miikkulainen R (1996) Efficient reinforcement learning through symbiotic evolution Mach Learn 22:11-32
Mukherjee S, Osuna E, Girosi F (1997) Nonlinear prediction of chaotic time series using support vector machines Proc IEEE NNSP :24-26
Muller KR, Smola AJ, Ratsch G, Sholkopf BS, Kohlmorgen J, Vapnik V (1997) Predicting time series with support vector machines: Proceedings of ICANN
Nolfi S, Floreano D, Miglino O, Mondada F (1994) Howto evolve autonomoussrobots: Different approaches in evolutionary robotics Proc 4th Intl Workshop Synthesis and Simulation of Living Systems, Brooks RA:Maes P, ed. pp.190
Pearlmutter BA (1995) Gradient calculation for dynamic recurrent neural networks: A survey IEEE Trans Neural Networks 6:1212-1228
Penrose R (1955) A generalized inverse for matrices Proc Cambridge Philosophy Soci 51:406-413
PĂ©rez-Ortiz JA, Gers FA, Eck D, Schmidhuber J (2003) Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets. Neural Netw 16:241-50 [Journal] [PubMed]
Potter MA, De_Jong KA (1995) Evolving neural networks with collaborative species Proc 1995 Summer Computer Simulation Conference :340-345
Rechenberg I (1973) Evolutionsstrategie Optimierung technischer Systeme nach Prinzipien der biologischen Evolution
Robinson AJ, Fallside F (1987) The utility driven dynamic error propagation network Tech Rep Cambridge University Engineering Department
Rumelhart D, Mccleland J (1986) Parallel Distributed Processing
Salomon J, King S, Osborne M (2002) Framewise phone classification using support vector machines Proc Intl Conf Spoken Language Process
Schmidhuber J (1990) Dynamische neuronale Netze und das fundamental eraumzeitliche Lernproblem Unpublished doctoral dissertation, Technische Universitat Munchen
Schmidhuber J (1992) A fixed size storage O(n3) time complexity learning algorithm for fully recurrent continually running networks Neural Comput 4:243-248
Schmidhuber J, Gagliolo M, Wierstra D, Gomez F (2006) Evolino for recurrent support vector machines Proc ESANN
Schmidhuber J, Gers F, Eck D (2002) Learning nonregular languages: a comparison of simple recurrent networks and LSTM. Neural Comput 14:2039-41 [Journal] [PubMed]
Schmidhuber J, Hochreiter S, Bengio Y (2001) Evaluating benchmark problems by random guessing A field guide to dynamical recurrent neural networks, Kremer SC:Kolen JF, ed.
Schmidhuber J, Wierstra D, Gomez FJ (2005) Evolino: Hybrid neuroevolution-optimal linear search for sequence prediction Proc 19th Intl Joint Conf Artif Intel
Schwefel HP (1977) Numerische Optimierung von Computer-Modellen
Schwefel HP (1995) Evolution and optimum seeking
Shimodaira H, Noma KI, Nakai M, Sagayama S (2002) Dynamic time alignment kernel in support vector machine Advances in neural information processing systems, Dietterich TG:Becker S:Shahramani Z, ed.
Siegelmann HT, Sontag ED (1991) Turing computability with neural nets Appl Math Lett 4:77-80
Sims K (1994) Evolving virtual creatures Proc SIGGRAPH :15-22
Suykens J, Vandewalle J (2000) Recurrent least squares support vector machines IEEE Transactions On Circuits And Systems 47:1109-1114
van_der_Zant T, Beacanovic V, Ishii K, Kobialka HU, Ploger PG (2004) Finding good echo state networks to control an underwater robot using evolutionary computations Proc 5th IFAC Symposium Intelligent Autonomous Vehicles
Vapnik V (1995) The Nature of Statistical Learning Theory
Werbos P (1989) Generalization of backpropagation with applications to a recurrent gas market model Neural Networks 1:339-356
Werbos PJ (1974) Beyond regression: New tools for prediction and analysis in the behavioral sciences Unpublished doctoral dissertation
Wierstra D, Gomez FJ, Schmidhuber J (2005) Modeling non-linear dynamical systems with Evolino Proc GECCO
Williams RJ (1989) Complexity of exact gradient computation algorithms for recurrent neural networks Tech Rep Northeastern University College of Computer Science
Yamauchi BM, Beer RD (1994) Sequential behavior and learning in evolved dynamical neural networks Adaptive Behav 2:219-246
Yao X (1993) A review of evolutionary artificial neural networks Intl J Intelligent Systems 8:539-567
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