Application of hybrid genetic algorithm to system identification

Wang, Grace S.

doi:10.1002/stc.306

Cited by 29 publications

(28 citation statements)

References 12 publications

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“…The existing denoising algorithms cannot remove stationary noise and nonstationary noise simultaneously, and these different types of noise should be dealt with separately. At present, the hybrid method has been widely used to solve this problem . To remove stationary noise and nonstationary noise separately, a hybrid method will be developed in this paper.…”

Section: Introductionmentioning

confidence: 99%

Identification of modal parameters from noisy transient response signals

Wang

Friswell

et al. 2017

Struct Control Health Monit

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Summary In the process of impact testing of large‐scale mechanical equipment, the measured forced response signals are often polluted by strong background noise. The forced response signal has a low signal‐to‐noise ratio, and this makes it difficult to accurately estimate the modal parameters. To solve this problem, the mean averaging of repeatedly measured frequency response function estimates is often employed in practical applications. However, a large number of impact tests are not practical for the modal testing of large‐scale mechanical equipment. The primary objective of this paper is to reduce the averaging operation and improve the accuracy of the modal identification by using a noise removal technique. A hybrid denoising method is proposed by combining the Wiener and improved minimum mean‐square‐error short‐time spectral amplitude estimators. The proposed method can effectively remove both stationary and highly nonstationary noise while preserving the important features of the true forced response signals. The simulation results show that the proposed noise removal technique improves the accuracy of the estimated modal parameters using only one impulse response signal. The experimental results show that the proposed method can accurately identify a natural frequency that is very close to a strong interference frequency in the modal test of a 600‐MW generator casing.

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Section: Introductionmentioning

confidence: 99%

Identification of modal parameters from noisy transient response signals

Wang

Friswell

et al. 2017

Struct Control Health Monit

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“…There are many other methods for system identification and parameter identification, all of which employed a good search algorithm. [25][26][27] Essentially, system identification is the process of determining parameters of a system based on numerical analysis of measurements of input and the corresponding output. The system parameter value, or state, is obtained or identified by minimizing the accumulated discrepancy between the recorded response and the identified response.…”

Section: Introductionmentioning

confidence: 99%

Improved memetic algorithm for nonlinear identification of a three-dimensional elliptical vibration cutting system

Zhou

2014

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article focuses on the nonlinear Wiener system identification of three-dimensional elliptical vibration cutting. The developed three-dimensional elliptical vibration cutting device is actuated by four piezoelectric stacks, which have hysteresis nonlinear characteristics. Our research proposes an improved memetic algorithm in order to identify the nonlinear elliptical vibration cutting model. The improved memetic algorithm displays the advantages of both particle swarm optimization and genetic algorithm, while simultaneously overcoming these programs' shortcomings. Moreover, it can quickly and efficiently search for the global optimization. The improved memetic algorithm's identification performance may be compared with the conventional memetic algorithm, particle swarm optimization, and genetic algorithm using two wellknown test functions. Test results demonstrate that the improved memetic algorithm can search the global optimal solution more efficiently than available state-of-the-art algorithms. Based on the input-output data collected from experiments, the accuracy of the identification model can potentially reach 97.55%. This relatively small margin of error verifies the efficiency of the proposed improved memetic algorithm for system identification.

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“…Thanks to the advances in computer technology over the last decades, with a tremendous increase in computational speed and memory size, methodologies based on heuristic algorithms have become more popular: in particular, genetic algorithms (GAs) , particle swarm optimization (PSO) , artificial neural networks , evolutionary strategy and differential evolution (DE) have gained great attention and recognition in the field of SI. These algorithms, even though facing some of the same issues as other TDMs (e.g., solution uniqueness in the case of an incomplete set of measurements), are conceptually simple, following laws taken from nature, and rely on performing a large number of iterations (forward analyses) to identify an ‘optimal’ solution in a search space.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous identification of structural parameters and dynamic input with incomplete output-only measurements

Sun

Betti

2013

Struct. Control Health Monit.

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SUMMARY A hybrid heuristic optimization strategy is presented to simultaneously identify structural parameters and, when possible, dynamic input time histories from incomplete sets of output measurements. The proposed strategy combines a novel swarm intelligence algorithm, the artificial bee colony algorithm, with a local search operator, Nelder–Mead simplex method, integrated in a search space reduction approach, so as to improve the convergence efficiency of the overall identification process. Because of the independent nature of the algorithm, a parallel scheme is implemented so as to improve the computational efficiency. If the time histories of the structural response and information about the mass of the structural system are available, then the algorithm can also be used for the identification of the time histories of the dynamic input force through a modified Newmark integration scheme, using the current estimates of the structural parameters. To investigate the applicability of the proposed technique, three numerical examples, two shear‐type building models and a coupled building system model under different conditions of data availabilities and noise corruption levels are presented. The results show that the proposed technique is powerful, robust and efficient in the simultaneous identification of the structural parameters and input force even from an incomplete set of noise‐contaminated structural response measurements. Copyright © 2013 John Wiley & Sons, Ltd.

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Application of hybrid genetic algorithm to system identification

Cited by 29 publications

References 12 publications

Identification of modal parameters from noisy transient response signals

Identification of modal parameters from noisy transient response signals

Improved memetic algorithm for nonlinear identification of a three-dimensional elliptical vibration cutting system

Simultaneous identification of structural parameters and dynamic input with incomplete output-only measurements

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