Spiking neural network and bull genetic algorithm for active vibration control

Research output: Contribution to journalArticle

12 Citations (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

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

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