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JMLR
2010
151views more  JMLR 2010»
14 years 4 months ago
Understanding the difficulty of training deep feedforward neural networks
Whereas before 2006 it appears that deep multilayer neural networks were not successfully trained, since then several algorithms have been shown to successfully train them, with e...
Xavier Glorot, Yoshua Bengio
TNN
2010
139views Management» more  TNN 2010»
14 years 4 months ago
Identification of finite state automata with a class of recurrent neural networks
A class of recurrent neural networks is proposed and proven to be capable of identifying any discrete-time dynamical system. The application of the proposed network is addressed in...
Sung Hwan Won, Iickho Song, Sun-Young Lee, Cheol H...
ICANN
2007
Springer
15 years 3 months ago
Self-perturbation and Homeostasis in Embodied Recurrent Neural Networks: A Meta-model and Some Explorations with Mechanisms for
Abstract. We present a model of a recurrent neural network, embodied in a minimalist articulated agent with a single link and joint. The configuration of the agent defined by one...
Jorge Simão
TR
2010
149views Hardware» more  TR 2010»
14 years 4 months ago
Health Condition Prediction of Gears Using a Recurrent Neural Network Approach
Abstract--The development of accurate health condition prediction approaches has been a key research topic in condition based maintenance (CBM) in recent years. However, current he...
Zhigang Tian, Ming J. Zuo
RAS
2000
136views more  RAS 2000»
14 years 9 months ago
A comparative study of soft-computing methodologies in identification of robotic manipulators
This paper investigates the identification of nonlinear systems by utilizing soft-computing approaches. As the identification methods, Feedforward Neural Network architecture (FNN...
Mehmet Önder Efe, Okyay Kaynak