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Functional extreme learning machine [PDF]

Frontiers in Computational Neuroscience, 2023
IntroductionExtreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional methods and yields promising performance.
Xianli Liu, Guo Zhou, Yongquan Zhou, Yongquan Zhou, Yongquan Zhou, Qifang Luo, Qifang Luo   +6 more
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Time efficient variants of Twin Extreme Learning Machine

Intelligent Systems with Applications, 2023
Twin Extreme Learning Machine models can obtain better generalization ability than the standard Extreme Learning Machine model. But, they require to solve a pair of quadratic programming problems for this.
Pritam Anand, Amisha Bharti, Reshma Rastogi   +2 more
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Gas Turbine Model Identification Based on Online Sequential Regularization Extreme Learning Machine with a Forgetting Factor

Energies, 2022
Due to the advantages of high convergence accuracy, fast training speed, and good generalization performance, the extreme learning machine is widely used in model identification. However, a gas turbine is a complex nonlinear system, and its sampling data
Rui Yang, Yongbao Liu, Xing He, Zhimeng Liu   +3 more
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Dual-Weighted Kernel Extreme Learning Machine for Hyperspectral Imagery Classification

Remote Sensing, 2021
Due to its excellent performance in high-dimensional space, the kernel extreme learning machine has been widely used in pattern recognition and machine learning fields.
Xumin Yu, Yan Feng, Yanlong Gao, Yingbiao Jia, Shaohui Mei   +4 more
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Multilayer Fisher extreme learning machine for classification

Complex & Intelligent Systems, 2022
As a special deep learning algorithm, the multilayer extreme learning machine (ML-ELM) has been extensively studied to solve practical problems in recent years.
Jie Lai, Xiaodan Wang, Qian Xiang, Jian Wang, Lei Lei   +4 more
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Unravelling an optical extreme learning machine [PDF]

EPJ Web of Conferences, 2022
Extreme learning machines (ELMs) are a versatile machine learning technique that can be seamlessly implemented with optical systems. In short, they can be described as a network of hidden neurons with random fixed weights and biases, that generate a ...
Silva Duarte, Silva Nuno A., Ferreira Tiago D., Rosa Carla C., Guerreiro Ariel   +4 more
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Experimenting with Extreme Learning Machine for Biomedical Image Classification

Applied Sciences, 2023
Currently, deep learning networks, with particular regard to convolutional neural network models, are typically exploited for biomedical image classification.
Francesco Mercaldo, Luca Brunese, Fabio Martinelli, Antonella Santone, Mario Cesarelli   +4 more
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Development and research of a neural network alternate incremental learning algorithm

Компьютерная оптика, 2023
In this paper, the relevance of developing methods and algorithms for neural network incremental learning is shown. Families of incremental learning techniques are presented. A possibility of using the extreme learning machine for incremental learning is
A.A. Orlov, E.S. Abramova
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Fault diagnosis method based on wavelet packet-energy entropy and fuzzy kernel extreme learning machine

Advances in Mechanical Engineering, 2018
Aiming at connatural limitations of extreme learning machine in practice, a new fault diagnosis method based on wavelet packet-energy entropy and fuzzy kernel extreme learning machine is proposed.
Jun Ma, Jiande Wu, Xiaodong Wang
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Research on an improved lp-RWMKE-ELM fault diagnosis model

工程科学学报, 2022
As the service time of military equipment increases, equipment failure data is continuously accumulated during events such as routine maintenance, training, and combat readiness exercises, and the data presented is often imbalanced to varying degrees and
Xing LIU, Jian-yin ZHAO, Min ZHU, Wei ZHANG   +3 more
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