Sciweavers

Free Online Productivity Tools i2Speak i2Symbol i2OCR iTex2Img iWeb2Print iWeb2Shot i2Type iPdf2Split iPdf2Merge i2Bopomofo i2Arabic i2Style i2Image i2PDF iLatex2Rtf Sci2ools

8

ESANN
2008

favoriteEmaildiscussreport

150views Neural Networks» more ESANN 2008»

Conditional prediction of time series using spiral recurrent neural network

13 years 6 months ago

Conditional prediction of time series using spiral recurrent neural network

Download www.dice.ucl.ac.be

Frequently, sequences of state transitions are triggered by specific signals. Learning these triggered sequences with recurrent neural networks implies storing them as different attractors of the recurrent hidden layer dynamics. A challenging test and also useful for application is conditional prediction of sequences giving just the trigger signal as an input and letting the recurrent neural network evolve the sequences automatically. This paper addresses this problem with the spiral recurrent neural network (SpiralRNN) architecture.

Huaien Gao, Rudolf Sollacher

Real-time Traffic

ESANN 2008 | Neural Networks | Recurrent Hidden Layer | Recurrent Neural Network | Sequences |

claim paper

Related Content

» Fading memory and time series prediction in recurrent networks with different forms of pla...

» A Neural Network Scheme for LongTerm Forecasting of Chaotic Time Series

» Prediction Using Recurrent Neural Network Based Fuzzy Inference system by the Modified Bee...

» A New Look at Nonlinear Time Series Prediction with NARX Recurrent Neural Network

» Autonomous learning algorithm for fully connected recurrent networks

» An EM Based Training Algorithm for Recurrent Neural Networks

» Time series prediction with recurrent neural networks trained by a hybrid PSOEA algorithm

» Health Condition Prediction of Gears Using a Recurrent Neural Network Approach

» Online SymbolicSequence Prediction with DiscreteTime Recurrent Neural Networks

Post Info
More Details (n/a)

Added	29 Oct 2010
Updated	29 Oct 2010
Type	Conference
Year	2008
Where	ESANN
Authors	Huaien Gao, Rudolf Sollacher

Comments (0)