Sample selection-based hierarchical extreme learning machine

Xiaoli, Xu; Li, Shan; Liang, Tu; Sun, Tongfeng

doi:10.1016/j.neucom.2019.10.013

Cited by 10 publications

(4 citation statements)

References 25 publications

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“…When training sample selection method is designed based on the fuzzy C-means clustering algorithm, and the proposed small training samples selection-based hierarchical ELM could reduce the computational time (Xu et al. 2020 ).…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Tensor based stacked fuzzy neural network for efficient data regression

Zhao

et al. 2022

Soft Comput

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Random vector functional link and extreme learning machine have been extended by the type-2 fuzzy sets with vector stacked methods, this extension leads to a new way to use tensor to construct learning structure for the type-2 fuzzy sets-based learning framework. In this paper, type-2 fuzzy sets-based random vector functional link, type-2 fuzzy sets-based extreme learning machine and Tikhonov-regularized extreme learning machine are fused into one network, a tensor way of stacking data is used to incorporate the nonlinear mappings when using type-2 fuzzy sets. In this way, the network could learn the sub-structure by three sub-structures’ algorithms, which are merged into one tensor structure via the type-2 fuzzy mapping results. To the stacked single fuzzy neural network, the consequent part parameters learning is implemented by unfolding tensor-based matrix regression. The newly proposed stacked single fuzzy neural network shows a new way to design the hybrid fuzzy neural network with the higher order fuzzy sets and higher order data structure. The effective of the proposed stacked single fuzzy neural network are verified by the classical testing benchmarks and several statistical testing methods.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Tensor based stacked fuzzy neural network for efficient data regression

Zhao

et al. 2022

Soft Comput

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“…The proposed CNN-RELM model outperforms CNN and RELM. Xu et al [39] constructed a sample selection-based hierarchical extreme learning machine (H-ELM) model for the classification task. They use a combination of FCM with CNN and H-ELM for data classification.…”

Section: Related Workmentioning

confidence: 99%

An Effective and Improved CNN-ELM Classifier for Handwritten Digits Recognition and Classification

Ali

Pei

et al. 2020

Symmetry

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Optical character recognition is gaining immense importance in the domain of deep learning. With each passing day, handwritten digits (0–9) data are increasing rapidly, and plenty of research has been conducted thus far. However, there is still a need to develop a robust model that can fetch useful information and investigate self-build handwritten digit data efficiently and effectively. The convolutional neural network (CNN) models incorporating a sigmoid activation function with a large number of derivatives have low efficiency in terms of feature extraction. Here, we designed a novel CNN model integrated with the extreme learning machine (ELM) algorithm. In this model, the sigmoid activation function is upgraded as the rectified linear unit (ReLU) activation function, and the CNN unit along with the ReLU activation function are used as a feature extractor. The ELM unit works as the image classifier, which makes the perfect symmetry for handwritten digit recognition. A deeplearning4j (DL4J) framework-based CNN-ELM model was developed and trained using the Modified National Institute of Standards and Technology (MNIST) database. Validation of the model was performed through self-build handwritten digits and USPS test datasets. Furthermore, we observed the variation of accuracies by adding various hidden layers in the architecture. Results reveal that the CNN-ELM-DL4J approach outperforms the conventional CNN models in terms of accuracy and computational time.

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“…Extreme Learning Machine (ELM), as a new feedforward neural network, has attracted wide attention due to its advantages of simple implementation and fast operation speed. [4] Therefore, in this paper, based on ELM, the typical oppressive interference singles are identified, and it is expected that a neural network with low computational complexity and high identification rate can be obtained through training, so as to promote the engineering application of this technology. [5] Given N arbitrarily different training dates ( )…”

Section: Introductionmentioning

confidence: 99%

A method for wireless communication interference signal identification based on extreme learning machine

Liu

Wang²,

Wang³

et al. 2023

Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022)

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Intelligent anti-jam communication is a new generation of anti-interference technology combined with artificial intelligence, and the identification of interference signals is the basis of the technology. It is required to achieve better identification results with lower computational complexity in engineering applications. However, previous research has shown that they cannot balance these two sides. Here, we report an interference signal identification algorithm based on Extreme Learning Machine (ELM). Five typical oppressive interference signals were recognized based on ELM which is based on feature extraction. The overall correct identification rate is more than 96% under the condition of 40 neurons in a single hidden layer, and it has certain generalization ability. This study objectively promotes the engineering application of this technology.

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Sample selection-based hierarchical extreme learning machine

Cited by 10 publications

References 25 publications

RETRACTED ARTICLE: Tensor based stacked fuzzy neural network for efficient data regression

RETRACTED ARTICLE: Tensor based stacked fuzzy neural network for efficient data regression

An Effective and Improved CNN-ELM Classifier for Handwritten Digits Recognition and Classification

A method for wireless communication interference signal identification based on extreme learning machine

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