Bouc–Wen hysteresis model identification using Modified Firefly Algorithm

Zaman, Mohammad Asif; Sikder, Urmita

doi:10.1016/j.jmmm.2015.07.080

Cited by 46 publications

(12 citation statements)

References 24 publications

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“…Three conventional kernels (see ) were used as the RBF, HPOL, and IPOL to find the best kernel function. There are many methods to obtain the optimal parameters of the kernel, such as BBO, 52,53 the artificial bee colony, 54,55 the firefly algorithm, 56,57 PSO, 58 or the genetic algorithm. 59,60 In this study, we used the grid-searching method due to its simplification.…”

Section: Methodsmentioning

confidence: 99%

Combination of stationary wavelet transform and kernel support vector machines for pathological brain detection

et al. 2016

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Finding an appropriate and accurate technology for early detection of disease is significantly important to research early treatments. We proposed some novel automatic classification systems based on the stationary wavelet transform (SWT) and the improved support vector machine (SVM). Magnetic Resonance Imaging (MRI) is commonly used for brain imaging as a non-invasive diagnostic tool to assist the pre-clinical diagnosis. However, MRI generates a large information set, which poses a challenge for classification. To deal with this problem we proposed a new approach, which combines SWT and Principal Component Analysis for feature extraction. In our experiments, three different datasets and four kinds of classifiers of the SVM were employed. The results over 5×6-fold stratified cross-validation (SCV) for Dataset-66, and 5×5-fold SCV for the other two datasets show that the average accuracy is almost 100.00%.

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Section: Methodsmentioning

confidence: 99%

Combination of stationary wavelet transform and kernel support vector machines for pathological brain detection

et al. 2016

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“…Due to the large number of parameters and the differential nature of the model, Modified Firefly Algorithm (MFA) [10] is used to identify the best parameters that fit the experimental curves.…”

Section: Modified Bouc-wen Identification By Modified Firefly Algorithmmentioning

confidence: 99%

Applicability study of a low cost seismic isolator prototype using recycled rubber

Muñoz

Castro-Dı́az²,

Reyna³

2019

TEC

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In order to protect buildings against earthquakes that are categorized as "common" according to the Peruvian Earthquake Resistant Standard, a prototype of Recycled Rubber Seismic Isolator ("RRSI") was developed in the structural laboratory of the Japan-Peru Center for Earthquake Engineering Research and Disaster Mitigation - CISMID, Peru. The raw material used to manufacture this device was recycled rubber tires; the rubber tire was cut into square shape sheets with 190mmx190mm of cross-section and a total thickness of around 11 mm. Rubber tire sheets were joined to each other by a vulcanization process, including rubber layers with 3mm of thickness made of recycled rubber tire powder in between rubber tire sheets; and in between the rubber tire sheet and the steel plate at both ends of the bearing. Two specimens were tested in a shaking table under a free vibration impulse or displacement in order to get their natural vibration frequency, natural period, and inherent damping. Then, to obtain the hysteretical behavior, a cyclic lateral reversal-loading test was conducted on three different specimens applying a constant axial load of 330MPa, 270MPa, and 220MPa respectively and a lateral displacement pattern with different levels of shear deformations up to the failure, which occurs at a shear strain of around 100%. From the experimental results, a nonlinear hysteretic behavior and energy dissipation were observed, decoupling the lateral movement. Finally, a numerical model was proposed to model the nonlinear hysteretic behavior of the RRSI based on a Modified Bouc-Wen model. This numerical model was simulated by using the specimens as base isolators for buildings.

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“…Here, our emphasis will be on the recent, new applications that can be representative in areas from engineering design to energy systems and from scheduling to image processing. For example, FA has been applied in the design of radial expanders in organic Rankine cycles [4], design optimization of steel frames [6], distributed generation system [7], beam design [12], wavelet neural network optimization [83], hysteresis model identification [84], detection of TEC seismo-ionospheric anomalies [2] and structural search in chemistry [3].…”

Section: Applications Of Fa and Its Variantsmentioning

confidence: 99%