Spiking neural network and bull genetic algorithm for active vibration control

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Systems with flexible structures display vibration as a characteristic property. However, when exposed to disturbing forces, then the component and/or structural nature of such systems are damaged. Therefore, this paper proposes two heuristics approaches to reduce the unwanted structural response delivered due to the external excitation; namely, bull genetic algorithm and spiking neural network. The bull genetic algorithm is based on a new selection property inherited from the bull concept. On the other hand, spiking neural network possess more than one synaptic terminal between each neural network layer and each synaptic terminal is modelled with a different period of delay. Extensive simulations have been conducted using simulated platform of a flexible beam vibration. To validate the proposed approaches, we performed a qualitative comparison with other related approaches such as traditional genetic algorithm, general regression neural network, bees algorithm, and adaptive neuro-fuzzy inference system. Based on the obtained results, it is found that the proposed approaches have outperformed other approaches, while bull genetic algorithm has a 5.2% performance improvement over spiking neural network.

Original languageEnglish
Pages (from-to)17-26
Number of pages10
JournalInternational Journal of Intelligent Systems and Applications
Volume10
Issue number2
DOIs
Publication statusPublished - Feb 1 2018

Fingerprint

Active Vibration Control
Spiking Neural Networks
Vibration control
Genetic algorithms
Genetic Algorithm
Neural networks
Vibration
Neural Networks
Flexible Beam
Adaptive Neuro-fuzzy Inference System
Flexible Structure
Flexible structures
Network layers
Fuzzy inference
Excitation
Regression
Vibrations (mechanical)
Heuristics
Simulation

Keywords

  • Bull genetic algorithm
  • Heuristics approaches
  • Spiking neural network

ASJC Scopus subject areas

  • Signal Processing
  • Modelling and Simulation
  • Human-Computer Interaction
  • Computer Science Applications
  • Computer Networks and Communications
  • Control and Optimization
  • Artificial Intelligence

Cite this

@article{e7dd4b976ee0441e99631a0e94343ef2,
title = "Spiking neural network and bull genetic algorithm for active vibration control",
abstract = "Systems with flexible structures display vibration as a characteristic property. However, when exposed to disturbing forces, then the component and/or structural nature of such systems are damaged. Therefore, this paper proposes two heuristics approaches to reduce the unwanted structural response delivered due to the external excitation; namely, bull genetic algorithm and spiking neural network. The bull genetic algorithm is based on a new selection property inherited from the bull concept. On the other hand, spiking neural network possess more than one synaptic terminal between each neural network layer and each synaptic terminal is modelled with a different period of delay. Extensive simulations have been conducted using simulated platform of a flexible beam vibration. To validate the proposed approaches, we performed a qualitative comparison with other related approaches such as traditional genetic algorithm, general regression neural network, bees algorithm, and adaptive neuro-fuzzy inference system. Based on the obtained results, it is found that the proposed approaches have outperformed other approaches, while bull genetic algorithm has a 5.2{\%} performance improvement over spiking neural network.",
keywords = "Bull genetic algorithm, Heuristics approaches, Spiking neural network",
author = "Awadalla, {Medhat H.A.}",
year = "2018",
month = "2",
day = "1",
doi = "10.5815/ijisa.2018.02.02",
language = "English",
volume = "10",
pages = "17--26",
journal = "International Journal of Intelligent Systems and Applications",
issn = "2074-904X",
publisher = "Modern Education and Computer Science Press",
number = "2",

}

TY - JOUR

T1 - Spiking neural network and bull genetic algorithm for active vibration control

AU - Awadalla, Medhat H.A.

PY - 2018/2/1

Y1 - 2018/2/1

N2 - Systems with flexible structures display vibration as a characteristic property. However, when exposed to disturbing forces, then the component and/or structural nature of such systems are damaged. Therefore, this paper proposes two heuristics approaches to reduce the unwanted structural response delivered due to the external excitation; namely, bull genetic algorithm and spiking neural network. The bull genetic algorithm is based on a new selection property inherited from the bull concept. On the other hand, spiking neural network possess more than one synaptic terminal between each neural network layer and each synaptic terminal is modelled with a different period of delay. Extensive simulations have been conducted using simulated platform of a flexible beam vibration. To validate the proposed approaches, we performed a qualitative comparison with other related approaches such as traditional genetic algorithm, general regression neural network, bees algorithm, and adaptive neuro-fuzzy inference system. Based on the obtained results, it is found that the proposed approaches have outperformed other approaches, while bull genetic algorithm has a 5.2% performance improvement over spiking neural network.

AB - Systems with flexible structures display vibration as a characteristic property. However, when exposed to disturbing forces, then the component and/or structural nature of such systems are damaged. Therefore, this paper proposes two heuristics approaches to reduce the unwanted structural response delivered due to the external excitation; namely, bull genetic algorithm and spiking neural network. The bull genetic algorithm is based on a new selection property inherited from the bull concept. On the other hand, spiking neural network possess more than one synaptic terminal between each neural network layer and each synaptic terminal is modelled with a different period of delay. Extensive simulations have been conducted using simulated platform of a flexible beam vibration. To validate the proposed approaches, we performed a qualitative comparison with other related approaches such as traditional genetic algorithm, general regression neural network, bees algorithm, and adaptive neuro-fuzzy inference system. Based on the obtained results, it is found that the proposed approaches have outperformed other approaches, while bull genetic algorithm has a 5.2% performance improvement over spiking neural network.

KW - Bull genetic algorithm

KW - Heuristics approaches

KW - Spiking neural network

UR - http://www.scopus.com/inward/record.url?scp=85041568932&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85041568932&partnerID=8YFLogxK

U2 - 10.5815/ijisa.2018.02.02

DO - 10.5815/ijisa.2018.02.02

M3 - Article

AN - SCOPUS:85041568932

VL - 10

SP - 17

EP - 26

JO - International Journal of Intelligent Systems and Applications

JF - International Journal of Intelligent Systems and Applications

SN - 2074-904X

IS - 2

ER -