[go: up one dir, main page]

Chang et al., 1995 - Google Patents

Tests for nonlinearity in short stationary time series

Chang et al., 1995

View PDF
Document ID
5518371711255122251
Author
Chang T
Sauer T
Schiff S
Publication year
Publication venue
Chaos: An Interdisciplinary Journal of Nonlinear Science

External Links

Snippet

Although it is relatively easy to generate chaotic dynamics from simple nonlinear equations, determining whether this type of nonlinear behavior is reflected in the behavior of an experimental system is a more difficult problem. Indirect measurements of detenninistic …
Continue reading at www.researchgate.net (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/06Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G06N3/0635Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means using analogue means

Similar Documents

Publication Publication Date Title
Chang et al. Tests for nonlinearity in short stationary time series
Dahlhaus et al. Identification of synaptic connections in neural ensembles by graphical models
Perunicic et al. Power quality disturbance detection and classification using wavelets and artificial neural networks
Tuckwell et al. Analytical and simulation results for stochastic Fitzhugh-Nagumo neurons and neural networks
Röschke et al. Nonlinear analysis of sleep EEG data in schizophrenia: calculation of the principal Lyapunov exponent
Liu et al. Temporally delayed linear modelling (TDLM) measures replay in both animals and humans
Pesonen et al. Treatment of missing data values in a neural network based decision support system for acute abdominal pain
Rapp Is there evidence for chaos in the human central nervous system?
Drapała et al. Two stage EMG onset detection method
Schiff et al. Discriminating deterministic versus stochastic dynamics in neuronal activity
Sahoo et al. Hierarchical extraction of functional connectivity components in human brain using resting-state fMRI
Small et al. Detecting nonlinearity in experimental data
Mitschke et al. Chaos versus noise in experimental data
Moser et al. Classification and detection of single evoked brain potentials using time-frequency amplitude features
Jeong et al. Test for low-dimensional determinism in electroencephalograms
Henry C Spike trains in a stochastic Hodgkin–Huxley system
Denton et al. Can the analytic techniques of nonlinear dynamics distinguish periodic, random and chaotic signals?
Aitken et al. Looking for chaos in brain slices
Zhao et al. Evidence consistent with deterministic chaos in human cardiac data: surrogate and nonlinear dynamical modeling
Khadra et al. Detecting chaos in HRV signals in human cardiac transplant recipients
Zochowski et al. Distributed and partially separate pools of neurons are correlated with two different components of the gill-withdrawal reflex in Aplysia
Prichard et al. Is the AE index the result of nonlinear dynamics?
Lovejoy et al. Apamin-induced irregular firing in vitro and irregular single-spike firing observed in vivo in dopamine neurons is chaotic
Sclabassi et al. Laboratory computers in neurophysiology
Kulkarni et al. Simulation of characteristics and artificial neural network modelingof electroencephalograph time series