Mapping some basic functions and operations to multilayer feedforward neural networks for modeling nonlinear dynamical systems and beyond

Pei, Jin‐Song; Eric, C.; Wright, Joseph P.; Masri, Sami F.

doi:10.1007/s11071-012-0667-9

Cited by 22 publications

(5 citation statements)

References 19 publications

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“…They explained physical meaning of trained networks' parameters for system identification and damage detection purpose. The approach presented in [27,28] is a continuation of Pei's studies, where they presented a developed initialization procedure and neurons' number selection to approximate nonlinear functions in context of restoring force planes identification.…”

Section: Artificial Neural Network-based Model For Identificationmentioning

confidence: 99%

Identification of the stick and slip motion between contact surfaces using artificial neural networks

et al. 2020

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The paper presents work related to nonlinear system parameters identification. The research is focused on systems with hysteretic stiffness characteristics. The identification procedure is developed with use of artificial neural networks. The presented method assumes two separate clusters of neural networks, which are supported by additional signal processing block. Such approach gives an advantage over the conventional identification methods due to its small restrictions. The validation process considers structural responses in time and frequency domains as well as the restoring force plane of the dynamic structure. First, verification of the identification method is performed on the numerical simulation of the system with hysteretic stiffness. Next, the identification of the real dynamic system with contact-related nonlinearity is carried out. The steel samples with contacting surfaces were used in the experiment. Electromagnetic shaker was used to excite the structure and enforce a relative shear motion between surfaces in contact. The system response was recorded using the Polytec laser vibrometer.

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Section: Artificial Neural Network-based Model For Identificationmentioning

confidence: 99%

Identification of the stick and slip motion between contact surfaces using artificial neural networks

et al. 2020

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“…The nonlinear restoring force is estimated through the displacement and velocity inputs. In Pei et al 147 and Pei and Masri, 148 ANN is used to estimate the displacement and acceleration transmissibility functions as well as the restoring force of a viscous fluid damper. In Ghiasi et al, 149 the ANN and the least square support vector machine (LS-SVM) 150 are employed to detect the damage location and severity.…”

Section: Data Fusion Techniques In Shmmentioning

confidence: 99%

Data fusion approaches for structural health monitoring and system identification: Past, present, and future

Jahanshahi

2018

Structural Health Monitoring

152

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During the past decades, significant efforts have been dedicated to develop reliable methods in structural health monitoring. The health assessment for the target structure of interest is achieved through the interpretation of collected data. At the beginning of the 21st century, the rapid advances in sensor technologies and data acquisition platforms have led to the new era of Big Data, where a huge amount of heterogeneous data are collected by a variety of sensors. The increasing accessibility and diversity of the data resources provide new opportunities for structural health monitoring, while the aggregation of information obtained from multiple sensors to make robust decisions remains a challenging problem. This article presents a comprehensive review of the recent data fusion applications in structural health monitoring. State-of-the-art theoretical concepts and applications of data fusion in structural health monitoring are presented. Challenges for data fusion in structural health monitoring are discussed, and a roadmap is provided for future research in this area.

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“…More specifically, [11] stated that a three-layered neural network with sigmoidal units in the hidden layer can approximate continuous or other defined functions which can be defined on compact sets with any precision. However, it is worth mentioning that the application of the fundamental approximation has a number of limitations: (i) the selected network architecture is somewhat arbitrarily, and (ii) the performance of neural networks depends on the data used in training and testing [12][13][14][15].…”

Section: Neural Network Algorithmsmentioning

confidence: 99%

Modeling of complex dynamic systems using differential neural networks with the incorporation of a priori knowledge

Bellamine

Almansoori

Elkamel

2015

Applied Mathematics and Computation

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Mapping some basic functions and operations to multilayer feedforward neural networks for modeling nonlinear dynamical systems and beyond

Cited by 22 publications

References 19 publications

Identification of the stick and slip motion between contact surfaces using artificial neural networks

Identification of the stick and slip motion between contact surfaces using artificial neural networks

Data fusion approaches for structural health monitoring and system identification: Past, present, and future

Modeling of complex dynamic systems using differential neural networks with the incorporation of a priori knowledge

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