Identification of nonlinear dynamic systems using differential evolution based update algorithms and Chebyshev functional link artificial neural network

Swayamsiddha, Swati; Mondal, Susovan; Thethi, H. Pal

doi:10.1049/cp.2013.2637

Cited by 4 publications

(2 citation statements)

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“…1 (b) presents the vicinity factor values for the reporting cells as well as the non-reporting cells for the given RCP problem. For example, the vicinity factor for cell number 4 which is a reporting cell is calculated by considering the neighbors that are non-reporting cells plus the reporting cell itself (1,2,3,7,8,9,13,5,6,12) which corresponds to the vicinity value of 11. Similarly while calculating the vicinity factor for a non-reporting cell we have to consider the maximum vicinity factor value among the reporting cells from where this cell can be reached.…”

Section: A System Modelmentioning

confidence: 99%

“…For solving the complex optimization problems the differential evolution algorithm has been used in various engineering applications and many works are also carried out to enhance the performance of this algorithm by introducing variants or by modifying it [5][6][7]. Literatures have been reported on nature inspired algorithms for mobility management [8,9].…”

Section: Introductionmentioning

confidence: 99%

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Optimal Reporting Cell Planning with Binary Differential Evolution Algorithm for Location Management Problem

Swayamsiddha¹,

Parija²,

Sahu³

et al. 2017

IJISA

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Abstract-This paper presents binary differential evolution based optimal reporting cell planning (RCP) for location management in wireless cellular networks. The significance of mobile location management (MLM) in wireless communication has evolved drastically due to tremendous rise in the number of mobile users with the constraint of limited bandwidth. The total location management cost involves signaling cost due to location registration and location search and a trade-off between these two gives optimal location management cost. The proposed binary differential evolution (BDE) algorithm is used to determine the optimal reporting cell planning configuration such that the overall mobility management cost is minimized. Evidently, from the simulation result the proposed technique works well for the reference networks in terms of optimal cost and convergence speed. Further the applicability of the BDE is also validated for the realistic network of BSNL (Bharat Sanchar Nigam Limited), Odisha.

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Section: A System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Reporting Cell Planning with Binary Differential Evolution Algorithm for Location Management Problem

Swayamsiddha¹,

Parija²,

Sahu³

et al. 2017

IJISA

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Performance Comparison of Variants of Hybrid FLANN-DE for Intelligent Nonlinear Dynamic System Identification

Swayamsiddha

2020

Advances in Intelligent Systems and Computing

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A Novel Neural Networks Ensemble Approach for Modeling Electrochemical Cells

Luzi

Paschero

Rizzi

et al. 2019

IEEE Trans. Neural Netw. Learning Syst.

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Accurate modeling of electrochemical cells is nowadays mandatory for achieving effective upgrades in the fields of energetic efficiency and sustainable mobility. Indeed, these models are often used for performing accurate State-of-Charge (SoC) estimations in energy storage systems used in microgrids or powering pure electric and hybrid cars. To this aim, a novel neural networks ensemble approach for modeling electrochemical cells is proposed in this paper. Herein, the system identification has been faced by means of a gray box technique, in which different and specialized neural networks are used for identifying the unknown internal behaviors of the cell. In particular, the a priori knowledge on the system dynamic is used for defining the network architecture. Specifically, each nonlinear function appearing in the system equations is approximated by a distinct neural network. The proposed model has been validated upon three different data sets both in terms of model accuracy and effectiveness in the SoC estimation task. The achieved performances have been compared with those of other computational intelligence approaches proposed in the literature. The results prove the effectiveness of the gray box scheme, achieving very promising performances in both the system identification accuracy and the SoC estimation task.

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Identification of nonlinear dynamic systems using differential evolution based update algorithms and Chebyshev functional link artificial neural network

Cited by 4 publications

References 0 publications

Optimal Reporting Cell Planning with Binary Differential Evolution Algorithm for Location Management Problem

Optimal Reporting Cell Planning with Binary Differential Evolution Algorithm for Location Management Problem

Performance Comparison of Variants of Hybrid FLANN-DE for Intelligent Nonlinear Dynamic System Identification

A Novel Neural Networks Ensemble Approach for Modeling Electrochemical Cells

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