Editorial Nonlinear Time Series 2013smart techniques which we hope can be regarded as a small step...
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Editorial Nonlinear Time Series 2013 Ming Li, 1 Carlo Cattani, 2 S. C. Lim, 3 and Massimo Scalia 4 1 School of Information Science & Technology, East China Normal University, Shanghai 200241, China 2 Department of Mathematics, University of Salerno, Via Ponte Don Melillo, 84084 Fisciano, Italy 3 Faculty of Engineering, Multimedia University, 63100 Cyberjaya, Selangor, Malaysia 4 CIRPS, University of Rome, “La Sapienza”, Piazza San Pietro in Vincoli 10, 00184 Rome, Italy Correspondence should be addressed to Ming Li; ming [email protected] Received 12 December 2013; Accepted 12 December 2013; Published 12 January 2014 Copyright © 2014 Ming Li et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Nonlinear time series plays a fundamental role in var- ious fields of sciences and engineering, ranging from physical and life sciences to telecommunication engineer- ing. One of the most challenging tasks of research is to face nonlinear problems which nonlinearly depend on a large number of parameters and/or are based on unpredictable, random, or fractal-like behavior. Some of the more intrigu- ing and modern topics such as telecommunication, traffic dynamics, complex networks, optimization, and fractional dynamics, can be described by nonlinear time series. How- ever, the extraction of useful information from the nonlinear time series by the commonly used analytical techniques is oſten difficult due the presence of noise in the signals. In this issue, we have selected papers on the main theme of identifying correct methods for the optimal understanding of the relevant information hidden in a signal representing a nonlinear phenomenon. We believe that there is a strong interest on this topic, although a widely accepted methodol- ogy for a proper investigation of nonlinear time series is still an open question. rough the collected papers of this issue we want to illustrate some of the interesting approaches and smart techniques which we hope can be regarded as a small step towards a systematic method for analyzing nonlinear time series. is special issue collects 16 papers with respect to nonlinear time series, its computations, and applications. M. Li and W. Zhao’s paper entitled “Convergence of sam- ple autocorrelation of long-range dependent traffic” exhibits a radical property of teletraffic time series. e paper “Iden- tification of nonstandard multifractional Brownian motions under white noise by multiscale local variations of its sample paths” by K. Ahn and I. Lee provides stable and simultaneous estimators of two parameters for nonstandard multifractional Brownian motions under white noise. M. Li’s paper “On the long-range dependence of fractional Brownian motion” clari- fies that fractional Brownian motion is long-range dependent on the Hurst parameter 0 < < 1. e paper by Li and Zhao, entitled “Wild fluctuations of random functions with the Pareto distribution,” introduces the terms wild and wildest fluctuations of time series that follow heavy-tailed distributions, such as the Pareto distribution. Peng et al.’s paper “Topology identification of complex network via chaotic ant swarm algorithm” studies the issue of topology identification from the point of view of param- eter optimization for complex networks. It is a pioneering work revealing an important phenomenon of Lorenz chaotic equation that was used in their simulation of the th node from a view of the golden ratio with respect to chaotic ant swarm algorithm. M. Pinchas’ two papers, entitled “Symbol error rate as a function of the residual ISI obtained by blind adaptive equalizers for the SIMO and fractional Gaussian noise case” and “Residual ISI obtained by nonblind adaptive equalizers and fractional noise,” provide examples of applying fractional Gaussian noise to communication engineering. ese two papers provide of a set of theories of adaptive equalizers essential to communication systems. e paper “Adaptive synchronization of complex dynamical multilinks Hindawi Publishing Corporation Mathematical Problems in Engineering Volume 2014, Article ID 492874, 2 pages http://dx.doi.org/10.1155/2014/492874
Transcript of Editorial Nonlinear Time Series 2013smart techniques which we hope can be regarded as a small step...
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EditorialNonlinear Time Series 2013
Ming Li,1 Carlo Cattani,2 S. C. Lim,3 and Massimo Scalia4
1 School of Information Science & Technology, East China Normal University, Shanghai 200241, China2Department of Mathematics, University of Salerno, Via Ponte Don Melillo, 84084 Fisciano, Italy3 Faculty of Engineering, Multimedia University, 63100 Cyberjaya, Selangor, Malaysia4CIRPS, University of Rome, “La Sapienza”, Piazza San Pietro in Vincoli 10, 00184 Rome, Italy
Correspondence should be addressed to Ming Li; ming [email protected]
Received 12 December 2013; Accepted 12 December 2013; Published 12 January 2014
Copyright © 2014 Ming Li et al. This is an open access article distributed under the Creative Commons Attribution License, whichpermits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Nonlinear time series plays a fundamental role in var-ious fields of sciences and engineering, ranging fromphysical and life sciences to telecommunication engineer-ing.
One of the most challenging tasks of research is to facenonlinear problems which nonlinearly depend on a largenumber of parameters and/or are based on unpredictable,random, or fractal-like behavior. Some of the more intrigu-ing and modern topics such as telecommunication, trafficdynamics, complex networks, optimization, and fractionaldynamics, can be described by nonlinear time series. How-ever, the extraction of useful information from the nonlineartime series by the commonly used analytical techniques isoften difficult due the presence of noise in the signals. Inthis issue, we have selected papers on the main theme ofidentifying correct methods for the optimal understandingof the relevant information hidden in a signal representinga nonlinear phenomenon. We believe that there is a stronginterest on this topic, although a widely accepted methodol-ogy for a proper investigation of nonlinear time series is stillan open question. Through the collected papers of this issuewe want to illustrate some of the interesting approaches andsmart techniques which we hope can be regarded as a smallstep towards a systematic method for analyzing nonlineartime series.
This special issue collects 16 papers with respect tononlinear time series, its computations, and applications.
M. Li and W. Zhao’s paper entitled “Convergence of sam-ple autocorrelation of long-range dependent traffic” exhibits
a radical property of teletraffic time series. The paper “Iden-tification of nonstandard multifractional Brownian motionsunder white noise by multiscale local variations of its samplepaths” by K. Ahn and I. Lee provides stable and simultaneousestimators of two parameters for nonstandardmultifractionalBrownian motions under white noise. M. Li’s paper “On thelong-range dependence of fractional Brownian motion” clari-fies that fractional Brownianmotion is long-range dependenton the Hurst parameter 0 < 𝐻 < 1. The paper by Liand Zhao, entitled “Wild fluctuations of random functionswith the Pareto distribution,” introduces the terms wild andwildest fluctuations of time series that follow heavy-taileddistributions, such as the Pareto distribution.
Peng et al.’s paper “Topology identification of complexnetwork via chaotic ant swarm algorithm” studies the issueof topology identification from the point of view of param-eter optimization for complex networks. It is a pioneeringwork revealing an important phenomenon of Lorenz chaoticequation that was used in their simulation of the 𝑖th nodefrom a view of the golden ratio with respect to chaotic antswarm algorithm. M. Pinchas’ two papers, entitled “Symbolerror rate as a function of the residual ISI obtained by blindadaptive equalizers for the SIMO and fractional Gaussiannoise case” and “Residual ISI obtained by nonblind adaptiveequalizers and fractional noise,” provide examples of applyingfractional Gaussian noise to communication engineering.These two papers provide of a set of theories of adaptiveequalizers essential to communication systems. The paper“Adaptive synchronization of complex dynamical multilinks
Hindawi Publishing CorporationMathematical Problems in EngineeringVolume 2014, Article ID 492874, 2 pageshttp://dx.doi.org/10.1155/2014/492874
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2 Mathematical Problems in Engineering
networks with similar nodes” by W. Wang et al. proposes anovel method of adaptive synchronization to describe thesimilarity of similar nodes in complex multilinks networks.The paper entitled “A reconfigurable logic cell based on asimple dynamical system” by L. Li et al. introduces a newscheme to achieve a dynamic logic gate that can be flexiblyvaried to obtain different logic functions by adjusting specificparameters of a dynamical system, discovering that it isresistant to system noise. In addition, the paper contributesa significant result in electronics engineering that the systemcan be considered as a leaky integrator.
O. C. Yolcu’s paper entitled “A hybrid fuzzy timeseries approach based on fuzzy clustering and artificial neu-ral network with single multiplicative neuron model” stud-ies fuzzy time series forecasting by using artificial neu-ral networks with single multiplicative neuron model inthe identification of fuzzy relation such that the archi-tecture selection problem can be eliminated without theneed for defuzzification step by constituting target val-ues from real observations of time series. The paper “AnARMA type fuzzy time series forecasting method basedon particle swarm optimization” by E. Egrioglu et al.discusses fuzzy time series forecasting. Its novelty in timeseries prediction is in presenting a novel first-order fuzzytime series by taking into account both autoregressive andmoving average structures. Its practical significance is thatthe proposed model is time invariant. The paper by C. Kocak“First-order ARMA type fuzzy time series method based onfuzzy logic relation tables” attempts to carry out fuzzy timeseries prediction a step further by considering autoregressivemoving average ARMA (1, 1) in order to eliminate thedeficiency of conventional fuzzy time series predictors. Thepaper “Set pair analysis based on phase space reconstructionmodel and its application in forecasting extreme temperature”by Y. Zhang et al. establishes a forecasting approach called setpair analysis based on phase space reconstruction model toimprove forecasting precision of a time series.
Z. Chen et al. in their paper “Sensor scheduling withintelligent optimization algorithm based on quantum theory”proposes an improved particle swarm optimization (PSO)algorithm using a best dimension mutation technique. M.Carlini et al.’s paper “Italian residential buildings: economicassessments for biomass boilers plants” deals with the eco-nomic feasibility of biomass boiler plants with specific regardto an existing residential building. It further investigates anItalian case that focuses on the attention on European andnational regulations on energy efficiency and considers therecent public incentives and supporting measures. The paperby S. Wanqing et al., “Tool wear detection using Lipschitzexponent and harmonic wavelet,” explains singularity analysiswith harmonic wavelet for data processing.
Acknowledgments
We are grateful to all the authors of this special issue fortheir contributions and to the reviewers for their valuablecomments. Ming Li acknowledges the supports for his
work in part by Macau Science and Technology Develop-ment Fund under Grant nos. 061/2011/A3, 009/2010/A1, and021/2009/A1 and by the National Natural Science Foundationof China under the Project Grant nos. 61272402, 61070214,and 60873264.
Ming LiCarlo Cattani
S. C. LimMassimo Scalia
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