Online weighted LS-SVM for hysteretic structural system identification

Tang, Hesheng; Xue, Shengjun; Chen, Rong; Sato, Tadanobu

doi:10.1016/j.engstruct.2006.03.008

Cited by 90 publications

(48 citation statements)

References 17 publications

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“…Currently, a wide range of analytical methods exist for linear or nonlinear system identification. Most common among these methods are the least squares method (Yang and Lin (2004); Tang et al (2006); Yang et al (2007)), the maximum likelihood method (Campillo and Mevel (2005)), the extended Kalman filter (Yang et al (2005)), the H ∞ filter method (Sato and Qi (1998)), and the particle filter method (Li et al (2004); Tang and Sato (2005)). Most of these methods require an initial guess to start the process.…”

Section: Introductionmentioning

confidence: 99%

Identification of Structural Systems Using Particle Swarm Optimization

Xue

Tang

Zhou

2009

Journal of Asian Architecture and Building Engineering

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Particle swarm optimization (PSO) is a new heuristic method that has yielded promising results for solving complex optimization problems. Its advantages are a simple structure, ease of use, quality of solution, and robustness. This paper utilizes the PSO algorithm for parameter estimation of structural systems, which could be formulated as a multi-modal numerical optimization problem with high dimension. Simulation results for identifying the parameters of multiple degree-of-freedom (DOF) linear and nonlinear structural systems are presented to demonstrate the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Identification of Structural Systems Using Particle Swarm Optimization

Xue

Tang

Zhou

2009

Journal of Asian Architecture and Building Engineering

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“…The Xt corresponded to the element with minimal absolute value in PCP is omitted from lYt by following steps: Note that the computational complexity of proposed algorithm is merely O(n), when it is applied in AKL, AKL is still recursive as in its former case because the matrix Kil used in recursion still could be computed recursively [15]. This characteristic fits the need of online learning modeling.…”

Section: ) Least Supported Sv Eliminationmentioning

confidence: 84%

Adaptive least contribution elimination kernel learning approach for rubber mixing soft-sensing modeling

Gao

Wang

et al. 2010

2010 IEEE International Conference on Intelligent Computing and Intelligent Systems

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Rubber mixing process is a typical non-linear fed batch process without well developed mechanism. Soft-sensing modeling of the mixture's Mooney viscosity is crucial and challenging since this index is an important process criterion to judge the quality of rubber compounds while the measurement of Mooney viscosity is time-consuming and laborious to assay.Furthermore, the mixing process is drifting and volatile even noisy; only few data samples could be used to modeling. In present paper, an adaptive least contribution elimination kernel learning (ALCEKL) approach is proposed to predict the Mooney viscosity. It adopts a sparsity strategy of least contribution elimination and presents a buffer based learning algorithm associated with improved space angle index (SAl) strategy. Experiments on field data indicate that proposed approach is more robust and accurate than other kernelized modeling methods with feasible computational complexity under field circumstances.

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“….N, where g l is the input data, o l is the output data and N is the number of sample points, LSSVM is governed by the following optimization problem [25]:…”

Section: State Identification Based On Lssvmmentioning

confidence: 99%