Chaotic time series prediction with employment of ant colony optimization

Gromov, Vasilii A.; Shulga, Artem N.

doi:10.1016/j.eswa.2012.01.171

Cited by 54 publications

(27 citation statements)

References 16 publications

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“…The second one seeks for typical dynamical patterns (variously known as typical sequences, [4][5][6] motifs, chunks, [7] shapelets, [8] patterns, subsequences, [3] etc.) in a time series observed.…”

Section: Related Workmentioning

confidence: 99%

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Active cluster replacement algorithm as a tool to assess bifurcation early-warning signs for von Karman equations

Gromov

Voronin

Gatylo

et al. 2017

AIR

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The paper deals with a novel algorithm used to improve identification quality of clusters generated by predictive clustering algorithm as a tool to identify states preceding to bifurcations for a system governed by von Karman equations. To construct bifurcation precursors, solutions (of the equations) observed on bifurcation paths are clustered; centers of the clusters constitute a set of bifurcation precursors. To decrease identification error rate, quality of each precursor is assessed with the employment of an additional, validation set. The paper concerns with two approaches to this procedure; the first one employs a single number to assess identification value of a cluster in order to delete those with low identification values. The second approach uses proposed knowledge extraction procedure to ascertain rules of replacement of the precursors chosen by the algorithm (active) by more efficient one. A wide-ranging simulation reveals that the best variant (provided that the Wishart clustering algorithm is utilized) is the replacement of the active cluster in conjunction local normalization of data. The optimal parameters values for both algorithms, arriving at essentially decreased identification errors.

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Section: Related Workmentioning

confidence: 99%

“…Some problems associated with application of the algorithm are discussed in Gromov and Shulga. [6] The modification used is based upon non-parametric estimate of k-neighbour probability density function:…”

Section: Sample Generation and Clustering Algorithmsmentioning

confidence: 99%

Active cluster replacement algorithm as a tool to assess bifurcation early-warning signs for von Karman equations

Gromov

Voronin

Gatylo

et al. 2017

AIR

Self Cite

View full text Add to dashboard Cite

show abstract

“…If the background of time series prediction is examined, it can be seen that previously introduced, traditional approaches or methods had failures for more complicated, new form of time series [5]. Because of this issue, there have been remarkable efforts to introduce and employ alternative solutions to overcome prediction problem on difficult -challenging time series.…”

Section: Introductionmentioning

confidence: 99%

“… The literature contains also many different studies that are based on employment of Ant Colony Optimization (ACO) to predict systems with chaotic flows [5,[21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Time Series Prediction With a Hybrid System Formed by Artificial Neural Network and Cognitive Development Optimization Algorithm

Köse

Arslan

2018

Scientia Iranica

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Time series prediction is a remarkable research interest, which is widely followed by scientists / researchers. Because many fields include analyzing processes over such time series, different kinds of approaches, methods, and techniques are often employed in order to achieve alternative prediction ways. It seems that Artificial Intelligence oriented solutions have strong potential on providing effective and accurate prediction approaches in even most complicated time series structures. In the sense of the explanations, this study aims to technique introduced by the authors. Here, it has been aimed to predict different kinds of time series, by using the introduced system / approach. In this way it has been possible to discuss about application potential of the hybrid system and report findings related to its success on prediction. The authors believe that the study has been a good chance to support the literature with an alternative solution approach and see potential of a newly developed, Artificial Intelligence oriented optimization algorithm on different applications.

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“…In the past few decades, a lot of swarm intelligence optimization algorithms, such as particle swarm optimization (PSO) [1], ant colony optimization (ACO) [2], bee colony optimization (BCO) [3], firefly optimization algorithms (FFO) [4], bacterial forging optimization (BFO) [5], artificial raindrop algorithm (ARA) [6], have been proposed to tackle increasingly complex real-world optimization problems in the field of intelligent control [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

An Improved Brain Storm Optimization with Dynamic Clustering Strategy

Cao

Xiao

2017

MATEC Web Conf.

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Abstract:Intelligence algorithms play an increasingly important role in the field of intelligent control. Brain storm optimization (BSO) is a new kind of swarm intelligence algorithm inspired by emulating the collective behavior of human beings in the problem solving process. To improve the performance of the original BSO, many variants of BSO are proposed. In this paper, an improved BSO algorithm with dynamic clustering strategy (BSO-DCS) is proposed as a variant of BSO for global optimization problems. The basic framework of BSO is firstly introduced. Then to reduce the time complexity of the original BSO, a new grouping method named dynamic clustering strategy (DCS) is proposed to improve the clustering method in the original BSO. To verify the effectiveness of the proposed BSO-DCS, it is tested on 12 benchmark functions of CEC 2005 with 30 dimensions. Experimental results show that DCS is an effective strategy to reduce the time complexity, and the improved BSO-DCS performs greatly better than the original BSO algorithm.

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Chaotic time series prediction with employment of ant colony optimization

Cited by 54 publications

References 16 publications

Active cluster replacement algorithm as a tool to assess bifurcation early-warning signs for von Karman equations

Active cluster replacement algorithm as a tool to assess bifurcation early-warning signs for von Karman equations

Time Series Prediction With a Hybrid System Formed by Artificial Neural Network and Cognitive Development Optimization Algorithm

An Improved Brain Storm Optimization with Dynamic Clustering Strategy

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