A Review of multilayer extreme learning machine neural networks

Vásquez-Coronel, José A.; Mora, Marco; Vilches, Karina

doi:10.1007/s10462-023-10478-4

Cited by 12 publications

(5 citation statements)

References 144 publications

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“…Huang et al presented a mathematical model for a singlehidden-layer feedforward ELM as shown in figure 2 that contains QUOTE N N neurons in the hidden layer and provides an output through function f N (•) which is represented by the following mathematical expression [38],…”

Section: Extreme Learning Machine (Elm)mentioning

confidence: 99%

See 1 more Smart Citation

Generalizing infrastructure inspection: step transfer learning aided extreme learning machine for automated crack detection in concrete structures

Sohaib,

Hasan,

Chen

et al. 2024

Meas. Sci. Technol.

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Identification of damage and selection of a restoration strategy in concrete structures is contingent upon automatic inspection for crack detection and assessment. Most research on deep learning models for autonomous inspection has focused solely on measuring crack dimensions, omitting the generalization power of a model. This research utilizes a novel step transfer learning aided extreme learning machine (STELM) approach to develop an automatic assessment strategy for surface cracks in concrete structures. Step transfer learning (STL) is helpful in mining generalized abstract features from different sets of source images, and extreme learning machine (ELM) helps the proposed model overcome the optimization limitations of traditional artificial neural networks. The proposed model achieved at least 2.5%, 4.8%, and 0.8% improvement in accuracy, recall, and precision, respectively, in comparison to the other studies, indicating that the proposed model could aid in the automated inspection of concrete structures, ensuring high generalization ability.

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Section: Extreme Learning Machine (Elm)mentioning

confidence: 99%

“…The following equation denotes the relationship between the function G i (•), and the activation function g (•). The operation is performed on a data representation for additive neurons through a radial basis function, respectively [38],…”

Section: Extreme Learning Machine (Elm)mentioning

confidence: 99%

Generalizing infrastructure inspection: step transfer learning aided extreme learning machine for automated crack detection in concrete structures

Sohaib,

Hasan,

Chen

et al. 2024

Meas. Sci. Technol.

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“…These highlight the need to investigate further the number of neurons in the hidden layer in future studies. Recently, two reviews of multilayer ELM neural networks [13,14] have been presented, highlighting the importance of implementing parallel and distributed computing to address big data problems with this variant. Finally, Patil and Sharma [12] provide a review of theories, algorithms, and applications of ELM, while Huérfano-Maldonado et al [11] present a comprehensive review of medical image processing with ELM.…”

Section: Elm Based On Metaheuristicsmentioning

confidence: 99%

A Review on Large-Scale Data Processing with Parallel and Distributed Randomized Extreme Learning Machine Neural Networks

Gelvez-Almeida,

Mora,

Barrientos

et al. 2024

MCA

Self Cite

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The randomization-based feedforward neural network has raised great interest in the scientific community due to its simplicity, training speed, and accuracy comparable to traditional learning algorithms. The basic algorithm consists of randomly determining the weights and biases of the hidden layer and analytically calculating the weights of the output layer by solving a linear overdetermined system using the Moore–Penrose generalized inverse. When processing large volumes of data, randomization-based feedforward neural network models consume large amounts of memory and drastically increase training time. To efficiently solve the above problems, parallel and distributed models have recently been proposed. Previous reviews of randomization-based feedforward neural network models have mainly focused on categorizing and describing the evolution of the algorithms presented in the literature. The main contribution of this paper is to approach the topic from the perspective of the handling of large volumes of data. In this sense, we present a current and extensive review of the parallel and distributed models of randomized feedforward neural networks, focusing on extreme learning machine. In particular, we review the mathematical foundations (Moore–Penrose generalized inverse and solution of linear systems using parallel and distributed methods) and hardware and software technologies considered in current implementations.

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“…The matrices H T H + 1 C I and H H T + 1 C I in (6) are non-singular matrices, since both H T H and H H T are positive semidefinite symmetric matrices and C > 0. When we remove the direct links from inputs to outputs in RVFL, the literature often presents this feedforward neural network as an ELM [55,56].…”

Section: Random Vector Functional Link Networkmentioning

confidence: 99%

“…• ELM and RVFL networks: These two non-iterative architectures learned nonlinear features thanks to the sigmoid function ϕ(z) = 1/(1 − exp(z)), chosen for its efficiency in this class of algorithms [56,66]. The random weights and biases in the hidden layer followed a uniform distribution in the [−1, 1] range [55].…”

Section: Selection Of Learning Models Based On Neural Networkmentioning

confidence: 99%

Congestive Heart Failure Category Classification Using Neural Networks in Short-Term Series

López,

Vásquez-Coronel

2023

Applied Sciences

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Congestive heart failure carries immense importance in the realm of public health. This significance arises from its substantial influence on the number of lives lost, economic burdens, the potential for prevention, and the opportunity to enhance the well-being of both individuals and the broader community through decision-making in healthcare. Several researchers have proposed neural networks for classification of different congestive heart failure categories. However, there is little information about the confidence of the prediction on short-term series. Therefore, evaluating classification models is required for effective decision-making in healthcare. This paper explores the use of three classical variants of neural networks to classify three groups of patients with congestive heart failure. The study considered the iterative method Multilayer Perceptron neural network (MLP), two non-iterative models (Extreme Learning Machine (ELM) and Random Vector Functional Link Network (RVFL)), and the CNN approach. The results showed that the deep feature learning system obtained better classification rates than MLP, ELM, and RVFL. Several scenarios designed by coupling some deep feature maps with the RVFL and MLP models showed very high simulation accuracy. The overall accuracy rate of CNN–MLP and CNN–RVFL varies between 98% and 99%.

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A Review of multilayer extreme learning machine neural networks

Cited by 12 publications

References 144 publications

Generalizing infrastructure inspection: step transfer learning aided extreme learning machine for automated crack detection in concrete structures

Generalizing infrastructure inspection: step transfer learning aided extreme learning machine for automated crack detection in concrete structures

A Review on Large-Scale Data Processing with Parallel and Distributed Randomized Extreme Learning Machine Neural Networks

Congestive Heart Failure Category Classification Using Neural Networks in Short-Term Series

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