33,473 research outputs found
Approximate entropy as an indicator of non-linearity in self paced voluntary finger movement EEG
This study investigates the indications of non-linear dynamic structures in electroencephalogram signals. The iterative amplitude adjusted surrogate data method along with seven non-linear test statistics namely the third order autocorrelation, asymmetry due to time reversal, delay vector variance method, correlation dimension, largest Lyapunov exponent, non-linear prediction error and approximate entropy has been used for analysing the EEG data obtained during self paced voluntary finger-movement. The results have demonstrated that there are clear indications of non-linearity in the EEG signals. However the rejection of the null hypothesis of non-linearity rate varied based on different parameter settings demonstrating significance of embedding dimension and time lag parameters for capturing underlying non-linear dynamics in the signals. Across non-linear test statistics, the highest degree of non-linearity was indicated by approximate entropy (APEN) feature regardless of the parameter settings
Exploring Transfer Function Nonlinearity in Echo State Networks
Supralinear and sublinear pre-synaptic and dendritic integration is
considered to be responsible for nonlinear computation power of biological
neurons, emphasizing the role of nonlinear integration as opposed to nonlinear
output thresholding. How, why, and to what degree the transfer function
nonlinearity helps biologically inspired neural network models is not fully
understood. Here, we study these questions in the context of echo state
networks (ESN). ESN is a simple neural network architecture in which a fixed
recurrent network is driven with an input signal, and the output is generated
by a readout layer from the measurements of the network states. ESN
architecture enjoys efficient training and good performance on certain
signal-processing tasks, such as system identification and time series
prediction. ESN performance has been analyzed with respect to the connectivity
pattern in the network structure and the input bias. However, the effects of
the transfer function in the network have not been studied systematically.
Here, we use an approach tanh on the Taylor expansion of a frequently used
transfer function, the hyperbolic tangent function, to systematically study the
effect of increasing nonlinearity of the transfer function on the memory,
nonlinear capacity, and signal processing performance of ESN. Interestingly, we
find that a quadratic approximation is enough to capture the computational
power of ESN with tanh function. The results of this study apply to both
software and hardware implementation of ESN.Comment: arXiv admin note: text overlap with arXiv:1502.0071
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The impact of nonlinear dynamics on the resilience of a grocery supply chain
Purpose of this paper: In an effort to improve operational and logistical efficiencies, UK grocery retailers combined primary and secondary distribution increasing the importance of designing resilient replenishment systems in the distribution centre. This paper has the purpose to analyse the resilience performance of the distribution centre stock ordering system within a grocery retailer. Design/methodology/approach: A system dynamics approach is used for framing and building a credible representation of the real system. Mathematical analysis of the nonlinear model based on nonlinear control engineering techniques in combination with system dynamics simulation have been used to understand the behaviour of stock and shipment output responses in the distribution centre given step and periodic demand signals. Findings: Preliminary mathematical analysis through nonlinear control theory techniques has been undertaken in order to gain initial insights in the understanding of the replenishment control model. This practice allowed the researcher to identify specific behaviour change in the DC stock and shipment responses, which are key indicators for assessing supply chain resilience, without going through a time-consuming simulation process. Transfer function analysis and describing function serve as a guideline for undertaking system dynamics simulation. Value: This paper aims to fill the gap in the literature of supply chain resilience by using quantitative system dynamics methods to assess the resilience performance of a grocery retailer. In this way, we also supplement the literature with empirical data. Moreover, we explore different analytical methods since simulation is the predominant method for quantitative analysis of system dynamics. Research limitations/implications (if applicable): This research is limited to the dynamics of single-echelon supply chain systems. Although the EPOS sales data and the store replenishment system have been considered in the validation process, this study has focused on analysing the resilience performance of the DC replenishment system only. Considering the multi-echelon supply chain is intended for further research activities. Practical implications (if applicable): The findings suggest that the distribution centre replenishment system can be re-designed in order to improve the supply chain resilience performance. The ‘As Is’ scenario produces slow response of stock levels and inventory targets are never recovered due to a permanent offset
Product Reservoir Computing: Time-Series Computation with Multiplicative Neurons
Echo state networks (ESN), a type of reservoir computing (RC) architecture,
are efficient and accurate artificial neural systems for time series processing
and learning. An ESN consists of a core of recurrent neural networks, called a
reservoir, with a small number of tunable parameters to generate a
high-dimensional representation of an input, and a readout layer which is
easily trained using regression to produce a desired output from the reservoir
states. Certain computational tasks involve real-time calculation of high-order
time correlations, which requires nonlinear transformation either in the
reservoir or the readout layer. Traditional ESN employs a reservoir with
sigmoid or tanh function neurons. In contrast, some types of biological neurons
obey response curves that can be described as a product unit rather than a sum
and threshold. Inspired by this class of neurons, we introduce a RC
architecture with a reservoir of product nodes for time series computation. We
find that the product RC shows many properties of standard ESN such as
short-term memory and nonlinear capacity. On standard benchmarks for chaotic
prediction tasks, the product RC maintains the performance of a standard
nonlinear ESN while being more amenable to mathematical analysis. Our study
provides evidence that such networks are powerful in highly nonlinear tasks
owing to high-order statistics generated by the recurrent product node
reservoir
Fast detection of nonlinearity and nonstationarity in short and noisy time series
We introduce a statistical method to detect nonlinearity and nonstationarity
in time series, that works even for short sequences and in presence of noise.
The method has a discrimination power similar to that of the most advanced
estimators on the market, yet it depends only on one parameter, is easier to
implement and faster. Applications to real data sets reject the null hypothesis
of an underlying stationary linear stochastic process with a higher confidence
interval than the best known nonlinear discriminators up to date.Comment: 5 pages, 6 figure
Renormalized entropy for one dimensional discrete maps: periodic and quasi-periodic route to chaos and their robustness
We apply renormalized entropy as a complexity measure to the logistic and
sine-circle maps. In the case of logistic map, renormalized entropy decreases
(increases) until the accumulation point (after the accumulation point up to
the most chaotic state) as a sign of increasing (decreasing) degree of order in
all the investigated periodic windows, namely, period-2, 3, and 5, thereby
proving the robustness of this complexity measure. This observed change in the
renormalized entropy is adequate, since the bifurcations are exhibited before
the accumulation point, after which the band-merging, in opposition to the
bifurcations, is exhibited. In addition to the precise detection of the
accumulation points in all these windows, it is shown that the renormalized
entropy can detect the self-similar windows in the chaotic regime by exhibiting
abrupt changes in its values. Regarding the sine-circle map, we observe that
the renormalized entropy detects also the quasi-periodic regimes by showing
oscillatory behavior particularly in these regimes. Moreover, the oscillatory
regime of the renormalized entropy corresponds to a larger interval of the
nonlinearity parameter of the sine-circle map as the value of the frequency
ratio parameter reaches the critical value, at which the winding ratio attains
the golden mean.Comment: 14 pages, 7 figure
Arnold maps with noise: Differentiability and non-monotonicity of the rotation number
Arnold's standard circle maps are widely used to study the quasi-periodic
route to chaos and other phenomena associated with nonlinear dynamics in the
presence of two rationally unrelated periodicities. In particular, the El
Nino-Southern Oscillation (ENSO) phenomenon is a crucial component of climate
variability on interannual time scales and it is dominated by the seasonal
cycle, on the one hand, and an intrinsic oscillatory instability with a period
of a few years, on the other. The role of meteorological phenomena on much
shorter time scales, such as westerly wind bursts, has also been recognized and
modeled as additive noise. We consider herein Arnold maps with additive,
uniformly distributed noise. When the map's nonlinear term, scaled by the
parameter , is sufficiently small, i.e. , the map is
known to be a diffeomorphism and the rotation number is a
differentiable function of the driving frequency . We concentrate on
the rotation number's behavior as the nonlinearity becomes large, and show
rigorously that is a differentiable function of ,
even for , at every point at which the noise-perturbed map is
mixing. We also provide a formula for the derivative of the rotation number.
The reasoning relies on linear-response theory and a computer-aided proof. In
the diffeomorphism case of , the rotation number
behaves monotonically with respect to . We show, using again a
computer-aided proof, that this is not the case when and the
map is not a diffeomorphism.Comment: Electronic copy of final peer-reviewed manuscript accepted for
publication in the Journal of Statistical Physic
Stochastic nonlinear Schrodinger equations driven by a fractional noise - Well posedness, large deviations and support
We consider stochastic nonlinear Schrodinger equations driven by an additive
noise. The noise is fractional in time with Hurst parameter H in (0,1). It is
also colored in space and the space correlation operator is assumed to be
nuclear. We study the local well-posedness of the equation. Under adequate
assumptions on the initial data, the space correlations of the noise and for
some saturated nonlinearities, we prove a sample path large deviations
principle and a support result. These results are stated in a space of
exploding paths which are Holder continuous in time until blow-up. We treat the
case of Kerr nonlinearities when H > 1/2
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