Topology Identification of Complex Network via Chaotic Ant Swarm Algorithm

Peng, Haipeng; Li, Lixiang; Kurths, Jürgen; Li, Shudong; Yang, Yixian

doi:10.1155/2013/401983

Cited by 5 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The unknown topology

can be successfully identified by the matrix

. Additionally, it can overcome many of the mentioned shortcomings of other methods [ 8 , 9 , 10 , 11 , 12 , 13 ], except for requiring huge computation as well as a priori knowledge about the topological parameters. In addition, it does not require that the coupling topology matrix to be undirected [ 34 , 35 ].…”

Section: Problem Formulationmentioning

confidence: 99%

“…Some initial results have been obtained. A number of the existing approaches, such as optimization-based strategies [ 7 , 8 , 9 , 10 , 11 , 12 ], compressed sensing technologies [ 13 , 14 , 15 , 16 ], and synchronization-based methods [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ], are widely used to deal with unknown topology. The main idea of an optimization-based strategy is to convert the TICDN into an optimization problem and then solve the problem with various classical or evolutionary algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…The main idea of an optimization-based strategy is to convert the TICDN into an optimization problem and then solve the problem with various classical or evolutionary algorithms. In [ 8 ], a chaotic ant swarm intelligence algorithm (CASIA) was investigated to identify topology. Other classical algorithms, such as weighted least square (WLS) algorithms, for the identification of the topology of uncertain networks were studied in [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Design of a Robust Synchronization-Based Topology Observer for Complex Delayed Networks with Fixed and Adaptive Coupling Strength

Sun,

Wu,

Chen

et al. 2024

Entropy

View full text Add to dashboard Cite

Network topology plays a key role in determining the characteristics and dynamical behaviors of a network. But in practice, network topology is sometimes hidden or uncertain ahead of time because of network complexity. In this paper, a robust-synchronization-based topology observer (STO) is proposed and applied to solve the problem of identifying the topology of complex delayed networks (TICDNs). In comparison to the existing literature, the proposed STO does not require any prior knowledge about the range of topological parameters and does not have strict limits on topology type. Furthermore, the proposed STO is suitable not only for networks with fixed coupling strength, but also for networks with adaptive coupling strength. Finally, a few comparison examples for TICDNs are used to verify the feasibility and efficiency of the proposed STO, and the results show that the proposed STO outperforms the other methods.

show abstract

“…The unknown topology

can be successfully identified by the matrix

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design of a Robust Synchronization-Based Topology Observer for Complex Delayed Networks with Fixed and Adaptive Coupling Strength

Sun,

Wu,

Chen

et al. 2024

Entropy

View full text Add to dashboard Cite

show abstract

“…Many learning algorithms have been used such as back-propagation algorithm, conjugate gradient algorithms, quasi-Newton algorithms, and Levenberg-Marquardt algorithm. Recently, new learning algorithm is used for training the ANN such as genetic algorithms GA [46][47][48], particle swarm optimization algorithm PSO [49][50][51], and chaotic ant swarm optimization algorithm CAS [52][53][54][55][56].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Accurate Fault Classifier and Locator for EHV Transmission Lines Based on Artificial Neural Networks

Hessine

Saber

2014

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The ability to identify the fault type and to locate the fault in extra high voltage transmission lines is very important for the economic operation of modern power systems. Accurate algorithms for fault classification and location based on artificial neural network are suggested in this paper. Two fault classification algorithms are presented; the first one uses the single ANN approach and the second one uses the modular ANN approach. A comparative study of two classifiers is done in order to choose which ANN fault classifier structure leads to the best performance. Design and implementation of modular ANN-based fault locator are presented. Three fault locators are proposed and a comparative study of the three fault locators is carried out in order to determine which fault locator architecture leads to the accurate fault location. Instantaneous current and/or voltage samples were used as inputs to ANNs. For fault classification, only the pre-fault and post-fault samples of three-phase currents were used. For fault location, pre-fault and post-fault samples of three-phase currents and/or voltages were used. The proposed algorithms were evaluated under different fault scenarios. Studied simulation results which are presented confirm the effectiveness of the proposed algorithms.

show abstract

“…Reference [21] used partial states observation and the parameter optimization method to identify the network parameters based on the objective function containing only available states. Reference [22] proposed the chaotic ant swarm algorithm to search (and identify) the parameters of the network. Van den Hof et al [23] treated the identification of network as a classical closed-loop system identification so that a module or sub-system can be identified.…”

Section: Introductionmentioning

confidence: 99%

Local time-varying topology identification of network with unknown parameters based on adaptive synchronization

2018

View full text Add to dashboard Cite

The network with some or all of characteristics including self-organization, self-similarity, attractor, small world and scale-free is referred to as a complex network. A complex network might demonstrate various uncertainties, such as the unknown node kinetics and the unknown network topology. The network topology identification using the observing output data of the network is a crucial step for complex network analysis, which is of great importance to understand the networks' properties and regulate network. In this paper, the output variable is used to drive the response network to identify partial time-varying topology of the drive network with unknown parameters. The synchronization between the response network and drive network and the successful identification are guaranteed by the Lyapunov stability theorem. The feasibility of the proposed method is demonstrated using two examples.Ovo je primjer sažetka. Ovo je primjer sažetka. Ovo je primjer sažetka. Ovo je primjer sažetka. Ovo je primjer sažetka. Ovo je primjer sažetka. Ovo je primjer sažetka. Ovo je primjer sažetka. Ovo je primjer sažetka. Ovo je primjer sažetka. Ovo je primjer sažetka. Ovo je primjer sažetka. Ovo je primjer sažetka. Ovo je primjer sažetka. Ovo je primjer sažetka.

show abstract

Topology Identification of Complex Network via Chaotic Ant Swarm Algorithm

Cited by 5 publications

References 14 publications

Design of a Robust Synchronization-Based Topology Observer for Complex Delayed Networks with Fixed and Adaptive Coupling Strength

Design of a Robust Synchronization-Based Topology Observer for Complex Delayed Networks with Fixed and Adaptive Coupling Strength

Accurate Fault Classifier and Locator for EHV Transmission Lines Based on Artificial Neural Networks

Local time-varying topology identification of network with unknown parameters based on adaptive synchronization

Contact Info

Product

Resources

About