Spiking Neural Network and Bull Genetic Algorithm for Active Vibration Control

Abstract

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.

A review of machine learning methods applied to structural dynamics and vibroacoustic

“…Chapter 2 of Duriez's book [199] briefly introduces MLC. Wangler and Hansen [200] were pioneers in applying MLC in active control of noise and vibration and were followed by many others in ANC [201][202][203][204][205][206][207] and AVC [208][209][210][211][212].…”

A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic

“…Chapter 2 of Duriez's book [282] has a gentle introduction to MLC. [119] was a pioneer to apply MLC in ANC and AVC and was followed by many others in acoustics [120][121][122][123][124][125][126] and vibration [127][128][129][130][131] control.…”