2-D defect profile reconstruction from ultrasonic guided wave signals based on QGA-kernelized ELM

Liu, Bing; Tang, Liwei; Wang, Jianbin; Li, Aihua; Hao, Yali

doi:10.1016/j.neucom.2012.11.053

Cited by 27 publications

(10 citation statements)

References 18 publications

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“…The standard gray wolf optimization algorithm uses a random method to generate the initial population, which is difficult to guarantee the diversity of the initial population. Besides, it also makes the algorithm poorly diverse and affects the global search ability [24]. To improve search efficiency and ensure the diversity of the initial population, this paper uses a great point set theory to generate the initial population.…”

Section: A: Improvement Of Population Initializationmentioning

confidence: 99%

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Features Fusion Exaction and KELM With Modified Grey Wolf Optimizer for Mixture Control Chart Patterns Recognition

Zhang

Wang

et al. 2020

IEEE Access

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Control charts are significant diagnostic tools to detect and identify the quality fluctuation of the complex industrial process. In the practical production process, attention is being paid to the monitoring of mixture control charts, which usually coupled by two or more basic control charts modes. This research is to present a hybrid pattern recognition method for mixture control charts. The proposed method mainly covers the feature fusion extraction (FFE) and kernel extreme learning machine (KELM) with modified grey wolf optimizer (MGWO). The FFE module applies the original data and their shape and statistical features as the features, then uses kernel entropy component analysis to reduce the feature dimension and extract valid features. One significant difficulty of KELM is to get suitable parameters like the penalty parameter and the kernel function parameter value. MGWO is established to the optimal tuning of KELM parameters, which improves the population initialization and nonlinear convergence factor of traditional grey wolf optimizer. The proposed methodology is promising to obtain a better classification recognition rate, less computational time and achieves more stable results in the pattern recognition problem of mixture control charts. INDEX TERMS Control chart patterns recognition, feature fusion extraction, kernel extreme learning machine, kernel entropy component analysis, modified grey wolf optimizer.

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Section: A: Improvement Of Population Initializationmentioning

confidence: 99%

“…Presently, numerous studies have been shown biologically-inspired methods (like genetic algorithm [23], [24] and particle swarm optimization [25]) for the KELM kernel function parameter. However, these methods have the weaknesses of premature convergence and a relatively high percentage of errors.…”

Section: Introductionmentioning

confidence: 99%

Features Fusion Exaction and KELM With Modified Grey Wolf Optimizer for Mixture Control Chart Patterns Recognition

Zhang

Wang

et al. 2020

IEEE Access

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“…Therefore, it needs to adopt an intelligent optimization algorithm to find the appropriate parameters to ensure the superior diagnosis performances of KELM. In recent years, some intelligent algorithms are widely applied in parameter optimization of KELM, such as genetic algorithm (GA), particle swarm optimization (PSO) [26][27][28]. However, these methods have the weaknesses of premature convergence and a relatively high percentage of errors.…”

Section: Introductionmentioning

confidence: 99%

Transformer Fault Diagnosis Technology Based on Maximally Collapsing Metric Learning and Parameter Optimization Kernel Extreme Learning Machine

Han

Shi

et al. 2022

IEEJ Transactions Elec Engng

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Aiming at the problem of unsatisfactory diagnosis performance of conventional fault diagnosis methods for transformer, a novel method based on maximally collapsing metric learning algorithm (MCML) and parameter optimization kernel extreme learning machine (KELM) is proposed in this study. First, a new set of dissolved gas analysis (DGA) features combination, which can reflect the transformer fault information, is used to form the input feature space. Then, the MCML is employed to reduce the feature space dimension to extract a set of optimal DGA features combination. Finally, the salp swarm algorithm (SSA) is utilized to optimize the parameters in KELM to establish an SSA-KELM model, which is adopted to diagnose and identify transformer faults. The proposed method is applied to the International Electrotechnical Commission (IEC) TC 10 database, and the results show that the feature extraction effect of MCML is superior than that of linear discriminant analysis, neighborhood preserving embedding, and Laplacian eigenmaps. The optimal DGA feature set is more advantageous than the frequent-used DGA data, IEC ratios, Rogers ratios, and Doernenburg Ratios. The diagnosis accuracy of SSA-KELM is better than that of KELM, particle swarm optimization-KELM, genetic algorithm-KELM, and loin swarm optimization-KELM. Furthermore, the generalization and robustness ability of the MCML and SSA-KELM is confirmed by the China DGA samples, the obtained results verify the reliability and validity of the proposed method again.

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“…When four typical micro-grids were applied to test the accuracy, it obtained relatively smaller predicting errors. Liu et al [22] proposed to use the quantum genetic algorithms (GA) optimized KELM for the reconstruction of 2-D profiles. By conducted some experiments, it was proved that it got faster speed, lower computational complexity and more excellent generalization performance than others.…”

Section: Introductionmentioning

confidence: 99%

Prediction Optimization of Cervical Hyperextension Injury: Kernel Extreme Learning Machines With Orthogonal Learning Butterfly Optimizer and Broyden- Fletcher-Goldfarb-Shanno Algorithms

et al. 2020

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In this research, X-ray and MRI images of patients suffering from cervical hyperextension injury are investigated. Also, radiographic images are collected from patients who suffered from trauma but without cervical hyperextension injury. The core engine algorithm of the optimized prediction model is kernel extreme learning machine (KELM), and the input data is 17 factors that may cause cervical hyperextension injury. As the optimization core, we utilized the Butterfly optimization algorithm (BOA). Up to now, few improved variants of BOA have been reported. The original BOA converges slowly and quickly falls into a locally optimal solution. An enhanced BOA based on orthogonal learning, Levy flight, and an exploitation engine is proposed in this paper to relieve these two shortcomings, which is called LBOLBOA. Orthogonal learning is utilized to construct guidance vectors for guiding agents toward the global optimum solution aiming to increase the accuracy of the solutions. Also, Levy flight and Broyden-Fletcher-Goldfarb-Shanno mechanisms are utilized to enrich the intensification propensities of BOA and stagnation avoidance. The proposed LBOLBOA is used to deal with continuous function optimization and machine learning problems, including parameter optimization of KELM. We rigorously verified this variant using a comprehensive set of the benchmark test suite and real-world dataset on cervical hyperextension injury. The results indicate that LBOLBOA can achieve improved performance in dealing with the function optimization and machine learning problems, especially the capability for prediction of cervical hyperextension injury.

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2-D defect profile reconstruction from ultrasonic guided wave signals based on QGA-kernelized ELM

Cited by 27 publications

References 18 publications

Features Fusion Exaction and KELM With Modified Grey Wolf Optimizer for Mixture Control Chart Patterns Recognition

Features Fusion Exaction and KELM With Modified Grey Wolf Optimizer for Mixture Control Chart Patterns Recognition

Transformer Fault Diagnosis Technology Based on Maximally Collapsing Metric Learning and Parameter Optimization Kernel Extreme Learning Machine

Prediction Optimization of Cervical Hyperextension Injury: Kernel Extreme Learning Machines With Orthogonal Learning Butterfly Optimizer and Broyden- Fletcher-Goldfarb-Shanno Algorithms

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