Ensemble of online sequential extreme learning machine

Yuan, Liang; Soh, Yeng Chai; Huang, Guang-Bin

doi:10.1016/j.neucom.2009.02.013

Cited by 305 publications

(132 citation statements)

References 12 publications

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“…From the last chapter we can know,the accuracy of OS-ELM algorithm is related to the initial setting of parameters, and different parameters have different effects on the accuracy of OS-ELM classifier [10] [11]. The parameters of artificial setting are not high, so the parameters are optimized by genetic algorithm.…”

Section: The Transformer Fault Diagnosis Based On Os-elm and Genetic mentioning

confidence: 99%

“…But in the implementation of the GA-oselm algorithm, these parameters do not need manual settings. Because the genetic algorithm simulates natural selection and natural genetic processes in replication, crossover and mutation process, a set of candidate solutions is retained in each iteration [13] [14]. The genetic operators (selection, crossover and mutation) are used to generate a new generation of candidate solutions.…”

Section: The Selection Of Os-elm and Genetic Algorithm Algorithm Paramentioning

confidence: 99%

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Optimizing Online Sequential Extreme Learning Machine Parameters and Application to Transformer Fault Diagnosis

Kang¹,

Chen²

2015

Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015

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Abstract. In order to solve the problem that the (OS-ELM) is used in the fault diagnosis of the transformer, the genetic algorithm (Algorithm Genetic) is applied to the on-line extreme learning machine, and a new method of transformer fault diagnosis is proposed. In this method, the number of hidden layer neurons of the Block L, the data set size N, and the hidden layer activation function are selected by the Algorithm Genetic optimization algorithm. Through simulation test, the fault diagnosis of transformer is 99.56%, and the test time is 0.0024 s. Compared with the optimization, the diagnostic accuracy and the test time of the transformer fault are improved obviously.

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Section: The Transformer Fault Diagnosis Based On Os-elm and Genetic mentioning

confidence: 99%

Section: The Selection Of Os-elm and Genetic Algorithm Algorithm Paramentioning

confidence: 99%

Optimizing Online Sequential Extreme Learning Machine Parameters and Application to Transformer Fault Diagnosis

Kang¹,

Chen²

2015

Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015

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“…The learning algorithm of ELM not only combines these activation functions within the hidden processing units, but also enhances the state-of-the-art approaches by speeding up the learning process of the algorithms and by avoiding local minima, which is one of the major drawbacks of gradient-based learning algorithms [15]. Several extensions to ELM have been introduced [20], [6], [26].…”

Section: Applied Procedures and Algorithmsmentioning

confidence: 99%

Quantifying the reliability of fault classifiers

Fink¹,

Zio²,

Weidmann³

2014

Information Sciences

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Fault diagnostics problems can be formulated as classification tasks. Due to limited data and to uncertainty, classification algorithms are not perfectly accurate in practical applications. Maintenance decisions based on erroneous fault classifications result in inefficient resource allocations and/or operational disturbances. Thus, knowing the accuracy of classifiers is important to give confidence in the maintenance decisions. The average accuracy of a classifier on a test set of data patterns is often used as a measure of confidence in the performance of a specific classifier. However, the performance of a classifier can vary in different regions of the input data space. Several techniques have been proposed to quantify the reliability of a classifier at the level of individual classifications. Many of the proposed techniques are only applicable to specific classifiers, such as ensemble techniques and support vector machines. In this paper, we propose a meta approach based on the typicalness framework (Kolmogorov's concept of randomness), which is independent of the applied classifier. We apply the approach to a case of fault diagnosis in railway turnout systems and compare the results obtained with both extreme learning machines and echo state networks.

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“…The invention of high-speed CCD cameras also enables fast image acquisition. In the 1980s, some organizations [3] A number of improved ELM-based algorithms have been developed, such as: I-ELM (incremental ELM) [20], OS-ELM (on-line sequential ELM) [21], EI-ELM (enhanced incremental ELM) [22], OP-ELM (Optimally Pruned ELM) [23], EM-ELM (esrror minimized ELM) [24], EOS-ELM (ensemble OS-ELM) [25], and so on. In the field of defect identification, ELM also plays a significant role.…”

Section: Introductionmentioning

confidence: 99%

An Algorithm for Surface Defect Identification of Steel Plates Based on Genetic Algorithm and Extreme Learning Machine

Tian

2017

Metals

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Abstract:Defects on the surface of steel plates are one of the most important factors affecting the quality of steel plates. It is of great importance to detect such defects through online surface inspection systems, whose ability of defect identification comes from self-learning through training samples. Extreme Learning Machine (ELM) is a fast machine learning algorithm with a high accuracy of identification. ELM is implemented by a hidden matrix generated with random initialization parameters, while different parameters usually result in different performances. To solve this problem, an improved ELM algorithm combined with a Genetic Algorithm was proposed and applied for the surface defect identification of hot rolled steel plates. The output matrix of the ELM's hidden layers was treated as a chromosome, and some novel iteration rules were added. The algorithm was tested with 1675 samples of hot rolled steel plates, including pockmarks, chaps, scars, longitudinal cracks, longitudinal scratches, scales, transverse cracks, transverse scratches, and roll marks. The results showed that the highest identification accuracies for the training and the testing set obtained by the G-ELM (Genetic Extreme Learning Machine) algorithm were 98.46% and 94.30%, respectively, which were about 5% higher than those obtained by the ELM algorithm.

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Ensemble of online sequential extreme learning machine

Cited by 305 publications

References 12 publications

Optimizing Online Sequential Extreme Learning Machine Parameters and Application to Transformer Fault Diagnosis

Optimizing Online Sequential Extreme Learning Machine Parameters and Application to Transformer Fault Diagnosis

Quantifying the reliability of fault classifiers

An Algorithm for Surface Defect Identification of Steel Plates Based on Genetic Algorithm and Extreme Learning Machine

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