Neural reconstruction of Lorenz attractors by an observable

Chaos, Solitons and Fractals

Volumn 14, Issue 1, 2002, Pages 81-86

  Cannas, B ^a   Cincotti, S ^b  

^a UNIVERSITY OF CAGLIARI   (Italy)

^b UNIVERSITY OF GENOA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ALGEBRA; CHAOS THEORY; NONLINEAR SYSTEMS; OBSERVABILITY; VECTORS;

LOCALLY RECURRENT NEURAL NETWORKS (LRNN); LORENZ ATTRACTORS;

NEURAL NETWORKS;

EID: 0036643128     PISSN: 09600779     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0960-0779(01)00206-5     Document Type: Article

References (20)

1. Learning a simple recurrent neural state space model to behave Chua's double scroll
2. Time series prediction by using a connectionist network with internal delay lines, time series predictions: Forecasting the future and understanding the past
3. Modeling nonlinear dynamics with neural networks: Examples in time series prediction
4. Performance analysis of locally recurrent neural networks
5. Learning of Chua's circuit attractors by locally recurrent neural networks
6. Detecting strange attractors in fluid turbulence
7. Communication systems via chaotic signals from reconstruction viewpoint
8. On global identifiability for arbitrary model parameterisation
9. An algebraic approach to linear and nonlinear control
10. Chaos synchronisation via sliding modes
11. A simplified gradient algorithm for IIR synapse multilayer perceptrons
12. FIR and IIR synapses, a new neural network architecture for time series modelling
13. A unifying view of some training algorithms for multilayer perceptrons with FIR filter synapses
14. Temporal backpropagation for FIR neural networks
15. Generalization of backpropagation to recurrent neural networks
16. Backpropagation through time: What it does and how to do it
17. On-line learning algorithms for locally recurrent neural networks
18. Deterministic non-periodic flow