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ICANN
2010
Springer
13 years 8 months ago
Reinforcement Learning Based Neural Controllers for Dynamic Processes without Exploration
Abstract. In this paper we present a Reinforcement Learning (RL) approach with the capability to train neural adaptive controllers for complex control problems without expensive on...
Frank-Florian Steege, André Hartmann, Erik ...
IJON
2007
99views more  IJON 2007»
13 years 7 months ago
Criticality of avalanche dynamics in adaptive recurrent networks
In many studies of self-organized criticality (SOC), branching processes were used to model the dynamics of the activity of the system during avalanches. This mathematical simpliï...
Anna Levina, Udo Ernst, J. Michael Herrmann
NEUROSCIENCE
2001
Springer
14 years 2 days ago
Finite-State Computation in Analog Neural Networks: Steps towards Biologically Plausible Models?
Abstract. Finite-state machines are the most pervasive models of computation, not only in theoretical computer science, but also in all of its applications to real-life problems, a...
Mikel L. Forcada, Rafael C. Carrasco
JIKM
2006
167views more  JIKM 2006»
13 years 7 months ago
Learning Trajectory Information with Neural Networks and the Markov Model to Develop Intelligent Location-Based Services
In the development of location-based services, various location-sensing techniques and experimental/commercial services have been used. However, conventional location-based service...
Sang-Jun Han, Sung-Bae Cho