Determining the input dimension of a neural network for nonlinear time series prediction

Determining the input dimension of a feed-forward neural network for nonlinear time series prediction plays an important role in the modelling.The paper first summarizes the current methods for determining the input dimension of the neural network.Then inspired by the fact that the correlation dimension of a nonlinear dynamic system is the most important feature of it ,the paper pressents a new idea that the input dimension of the neural network for nonlinear time series prediction can be taken as an integer just greater than or equal to the correlation dimension.Fimally,some validation examples and results are given.     

Determining the minimum embedding dimension of nonlinear time series based on prediction method

Determining the embedding dimension of nonlinear time series plays an important role in the reconstruction of nonlinear dynamics. The paper first summarizes the current methods for determining the embedding dimension. Then, inspired by the fact that the optimum modelling dimension of nonlinear autoregressive (NAR) prediction model can characterize the embedding feature of the dynamics, the paper presents a new idea that the optimum modelling dimension of the NAR model can be taken as the minimum embedding dimension. Some validation examples and results are given and the present method shows its advantage for short data series.     

Fifth-order phase-sensitive detection of ultrafast polarization beats in cascade four-level system

We study the colour-locked twin-noisy-field correlation effects in the fifth-order nonlinear susceptibility of ultrafast polarization beats in a cascade four-level system. More importantly, the fifth-order phase-sensitive heterodyne detection of ultrafast polarization beats has been exploited. The fifth-order nonlinear optical response can be controlled and modified through the colour-locked correlation of twin noisy fields. Thus, this method with the phase dispersion information is a good way to measure the real and imaginary parts of the fifth-order nonlinear susceptibility.     

Detection of air target based on multi-fractal analysis in a laser radar

A novel approach for detecting laser radar target based on multi-fraction dimension (MFD) of laser clutters has been proposed. The fractal dimension (FD) of laser clutter and the echoes from the plan are estimated using box-counting dimension algorithm. The intrinsic difference in the spectrum of FD Dq between them is extracted and used to detect targets. Experimental results show that the method based on MFD is more reliable than that on FD and can improve the accuracy of detection.     

A new method of determining the optimal embedding dimension based on nonlinear prediction

A new method is proposed to determine the optimal embedding dimension from a scalar time series in this paper. This method determines the optimal embedding dimension by optimizing the nonlinear autoregressive prediction model parameterized by the embedding dimension and the nonlinear degree. Simulation results show the effectiveness of this method. And this method is applicable to a short time series, stable to noise, computationally efficient, and without any purposely introduced parameters.     

Self-sustained firing activities of the cortical network with plastic rules in weak AC electrical fields

Both external and endogenous electrical fields widely exist in the environment of cortical neurons. The effects of a weak alternating current (AC) field on a neural network model with synaptic plasticity are studied. It is found that self-sustained rhythmic firing patterns, which are closely correlated with the cognitive functions, are significantly modified due to the self-organizing of the network in the weak AC field. The activities of the neural networks are affected by the synaptic connection strength, the external stimuli, and so on. In the presence of learning rules, the synaptic connections can be modulated by the external stimuli, which will further enhance the sensitivity of the network to the external signal. The properties of the external AC stimuli can serve as control parameters in modulating the evolution of the neural network.     

Correlation dimension based nonlinear analysis of network traffics with different application protocols

This paper uses a correlation dimension based nonlinear analysis approach to analyse the dynamics of network traffics with three different application protocols-HTTP,FTP and SMTP.First,the phase space is reconstructed and the embedding parameters are obtained by the mutual information method.Secondly,the correlation dimensions of three different traffics are calculated and the results of analysis have demonstrated that the dynamics of the three different application protocol traffics is different from each other in nature,i.e.HTTP and FTP traffics are chaotic,furthermore,the former is more complex than the later;on the other hand,SMTP traffic is stochastic.It is shown that correlation dimension approach is an efficient method to understand and to characterize the nonlinear dynamics of HTTP,FTP and SMTP protocol network traffics.This analysis provided insight into and a more accurate understanding of nonlinear dynamics of internet traffics which have a complex mixture of chaotic and stochastic components.     

Third-Order Nonlinear Optical Properties of a Series of Polythiophenes

The third-order nonlinear optical properties of a series of polythiophenes are investigated with the Z-scan method under picosecond pulse laser irradiation at 532nm. The copolymers exhibit a good nonlinear response： large nonlinear refraction coefficients without nonlinear absorption. The signs of nonlinear refraction coefficients are positive, which are opposite to the negative signs of the polythiophene reported before. The mechanism accounting for the process of nonlinear refraction under pulse laser excitation is analyzed from the viewpoint of the electrondonor/acceptor units of polythiophenes. Moreover, changes of nonlinearity according to the lengths of main chains in polythiophene molecules are discussed.     

Correlation between detrended fluctuation analysis and the Lempel–Ziv complexity in nonlinear time series analysis

We focus on the study of the correlation between the detrended fluctuation analysis (DFA) and the Lempel-Ziv complexity (LZC) in nonlinear time series analysis in this paper. Typical dynamical systems including logistic map and Duffing model are investigated. Moreover, the influences of the Gaussian random noise on both DFA and LZC are analyzed. The results show a high correlation between DFA and LZC, which can quantify the non-stationarity and the nonlinearity of the time series, respectively. With the enhancement of the random component, the exponent α and the normalized complexity index C show increasing trends. In addition, C is found to be more sensitive to the fluctuation in the nonlinear time series than α. Finally, the correlation between DFA and LZC is applied to the feature extraction of vibration signals for a reciprocating compressor gas valve, and an effective fault diagnosis result is obtained.     

Global vector-field reconstruction of nonlinear dynamical systems from a time series with SVD method and validation with Lyapunov exponents

A method for the global vector-field reconstruction of nonlinear dynamical systems from a time series is studied in this paper. It employs a complete set of polynomials and singular value decomposition (SVD) to estimate a standard function which is certtral to the algorithm. Lyapunov exponents and dimension, calculated from the differential equations of a standard system, are used for the validation of the reconstruction. The algorithm is proven to be practical by applying it to a Roessler system.     

Nonlinear response and chaos in semiconductors induced by impact ionization

A review is given of the nonlinear response and chaos induced by impact ionization of neutral shallow donors, observed in n-GaAs. Two kinds of the observation are described; (i) firing wave instability, and (ii) periodically driven current filament. For the firing wave instability, several important aspects are discussed including the selective excitation of the current filaments and the deterministic nature of the firing density wave. The nonlinear response of a periodically driven current filament has been investigated by applying a dc+ac bias of the form ofV _dc+V _ac sin(2f ₀ t), wheref ₀1 MHz. The carrier dynamics and the bifurcation routes to chaos are discussed in terms of the observed phase diagram and the bifurcation map. The deterministic nature of the strange attractors are described in detail in terms of the correlation dimension and the Kolmogorov entropy.     

Synchronization in coupled nonidentical incommensurate fractional-order systems

Synchronization of fractional-order nonlinear systems has received considerable attention for many research activities in recent years. In this Letter, we consider the synchronization between two nonidentical fractional-order systems. Based on the open-plus-closed-loop control method, a general coupling applied to the response system is proposed for synchronizing two nonidentical incommensurate fractional-order systems. We also derive a local stability criterion for such synchronization behavior by utilizing the stability theory of linear incommensurate fractional-order differential equations. Feasibility of the proposed coupling scheme is illustrated through numerical simulations of a limit cycle system, a chaotic system and a hyperchaotic system.     

Noise Effects on Temperature Encoding of Neuronal Spike Trains in a Cold Receptor

We examine how noise interacts with encoding mechanisms of neuronal stimulus in a cold receptor. From ISI series and bifurcation diagrams it is shown that there are considerable differences in interval distributions and impulse patterns caused by purely deterministic simulations and noisy simulations. The ISI-distance can be used as an effective and powerful way to measure the noise effects on spike trains of the cold receptor quantitatively. It is also found that spike trains observed in cold receptors can be more strongly affected by noise for low temperatures than for high temperatures in some aspects; meanwhile, the spike train has greater variability with increasing noise intensity.     

Correlation measure to detect time series distances, whence economy globalization

An instantaneous time series distance is defined through the equal time correlation coefficient. The idea is applied to the Gross Domestic Product (GDP) yearly increments of 21 rich countries between 1950 and 2005 in order to test the process of economic globalisation. Some data discussion is first presented to decide what (EKS, GK, or derived) GDP series should be studied. Distances are then calculated from the correlation coefficient values between pairs of series. The role of time averaging of the distances over finite size windows is discussed. Three network structures are next constructed based on the hierarchy of distances. It is shown that the mean distance between the most developed countries on several networks actually decreases in time, —which we consider as a proof of globalization. An empirical law is found for the evolution after 1990, similar to that found in flux creep. The optimal observation time window size is found ?15 years.     

Synchronization and control of chaos in coupled chaotic multimode Nd:YAG lasers

In this Letter we numerically investigate the dynamics of a system of two coupled chaotic multimode Nd:YAG lasers with two mode and three mode outputs. Unidirectional and bidirectional coupling schemes are adopted; intensity time series plots, phase space plots and synchronization plots are used for studying the dynamics. Quality of synchronization is measured using correlation index plots. It is found that for laser with two mode output bidirectional direct coupling scheme is found to be effective in achieving complete synchronization, control of chaos and amplification in output intensity. For laser with three mode output, bidirectional difference coupling scheme gives much better chaotic synchronization as compared to unidirectional difference coupling but at the cost of higher coupling strength. We also conclude that the coupling scheme and system properties play an important role in determining the type of synchronization exhibited by the system.     

心率变异性分析在新生儿疼痛检测中的应用

为了研究疼痛暴露对新生儿自主神经系统的影响,并建立基于心率变异性(heart rate variability,HRV)指标的新生儿疼痛检测模型,采用时域、频域和非线性方法对40名新生儿疼痛暴露前后的心电数据进行短时HRV分析,Wilcoxon符号秩检验用于统计分析,支持向量机(support vector machine,SVM)用于建立检测模型.结果表明,RR间期均值a RR、低频段功率LF、高频段功率HF等3个线性指标和近似熵Ap En、样本熵Samp En、递归率REC等9个非线性指标在疼痛前后具有统计学差异;基于a RR、相邻两个RR间期对差值大于50 ms的百分比p NN50,Ap En,关联维D2和REC等5个指标和SVM的疼痛检测模型检测正确率达到83.75%.HRV的相关指标可反映新生儿自主神经系统对疼痛暴露的应答,基于HRV指标和SVM的模型可用于新生儿疼痛检测.     

Test for low-dimensional determinism in electroencephalograms

We tested low-dimensional determinism in an electroencephalogram (EEG), based on the fact that smoothness (continuity) on an embedded phase space is enough to imply determinism within time series. A modified version of the method developed by Salvino and Cawley [Phys. Rev. Lett. 73, 1091 (1994)] was used. In our method, we chose a box randomly and then estimated the mean directional element in the box containing the d+1 data points, where d is the embedding dimension. The global average for the mean local directional elements over the boxes, W, is a measure for smoothness. The nonlinear noise reduction method developed by Sauer [Physica D 58, 193 (1992)] is then applied to the EEG. We also compared the results for the EEG with those for its surrogate data. We found that the W values for the noise-reduced EEG had stable values around 0.35, which means that the EEG is not a low-dimensional deterministic signal. However, this method may not be applicable to the time series generated from high-dimensional deterministic systems. We cannot exclude the possibility that the determinism in the EEG may be too high-dimensional to be detected with current methods.     

基于复杂度的针刺脑电信号特征提取

为探究针灸刺激对大脑活动产生的影响,文章设计了4种针刺频率针刺右腿足三里穴获取脑电的实验.首次采用排序递归图和关联维数方法提取针刺脑电信号的复杂度参数来反映针刺大脑的功能状态,并基于这些方法研究了针刺作用对大脑功能区域的影响以及不同针刺频率与脑电复杂度的相关性.发现针刺时脑电的复杂度高于针刺前,尤以频率为100次/min的针刺影响最为明显;从FP2, F7, T3导联脑电中提取的确定性指标(DET)可作为区分针刺状态与针刺前状态的一种特征参数. 关键词： 针灸 脑电 排序递归图 关联维数     

Scale-dependent nature of the surface fractal dimension for bi- and multi-disperse porous solids by mercury porosimetry

The surface fractal dimension was calculated by using a mathematical model and mercury intrusion data for a variety of bi- and multi-disperse porous solids including silica gels, alumina pellets, and building stones. The mathematical model was obtained by modifying the well-established scaling relation published previously [Ind. Eng. Chem. Res., 34 (1995) 1383-1386]. It was also verified by comparing with the theoretical surface fractal dimensions for regular fractal structures (Skerpinski tetrahedron and Menger sponge) and the calculated surface fractal dimensions for silica gel and alumina particles via the linear fitting method established previously. The calculation results for various bi- and multi-disperse porous solids have demonstrated that the scale-dependent nature of the surface fractal dimension is ubiquitous. The difference in the surface fractal dimension between pore size intervals usually exists. The estimation of the surface fractal dimension on an average stand may lead to erroneous results.