Evolving modular fast-weight networks for control

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3697 LNCS, Issue , 2005, Pages 383-389

  Gomez, Faustino ^a   Schmidhuber, Jürgen ^a,b  

^a DALLE MOLLE INSTITUTE FOR ARTIFICIAL INTELLIGENCE IDSIA   (Switzerland)

^b TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

MAPPING; NEURAL NETWORKS; PROBLEM SOLVING; ROBOTS;

FAST-WEIGHT NETWORKS; LINEAR CONTROL; NON-LINEAR MAPPINGS; OPERATING MODES;

CONTROL SYSTEMS;

EID: 33646241314     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: None     Document Type: Conference Paper

References (7)

1. Rugh, W., Shamma, J.: A survey of research on gain-scheduling. Automatica (2000) 1401-1425
2. Yao, X.: Evolving artificial neural networks. Proceedings of the IEEE 87 (1999) 1423-1447
3. Kretchmar, R.M.: A Synthesis of Reinforcement Learning and Robust Control Theory. PhD thesis, Department of Computer Science, Colorado State University, Fort Collins, Colorado (2000)
4. Hinton, G.E., Plaut, D.C.: Using fast weights to deblur old memories. In: Proceedings of the Ninth Annual Cognitive Science Society Conference, Hillsdale, NJ, Lawrence Erlbaum Associates (1987) 177-186
5. Schmidhuber, J.: Learning to control fast-weight memories: An alternative to dynamic recurrent networks. Neural Computation 4 (1992) 131-139
6. Gomez, F.J., Schmidhuber, J.: Co-evolving recurrent neurons learn deep memory pomdps. Technical Report 17-04, IDSIA, Lugano, Switzerland (2004)
7. Gomez, F., Miikkulainen, R.: 2-D pole-balancing with recurrent evolutionary networks. In: Proceedings of the International Conference on Artificial Neural Networks, Berlin; New York, Springer-Verlag (1998) 425-430