Evolutionary extreme learning machine based on an improved MOPSO algorithm

Extreme learning machine (ELM), as a single hidden layer feedforward neural network (SLFN), has attracted extensive attention because of its fast learning speed and high accuracy. However, the random selection of input weights and hidden biases is the main reason that deteriorates the generalization...

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Veröffentlicht in:	Neural computing & applications Jg. 37; H. 12; S. 7733 - 7750
Hauptverfasser:	Ling, Qinghua, Tan, Kaimin, Wang, Yuyan, Li, Zexu, Liu, Wenkai
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.04.2025 Springer Nature B.V
Schlagworte:	Algorithms Archives & records Artificial Intelligence Artificial neural networks Bias Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Convergence Data Mining and Knowledge Discovery Evolutionary algorithms Image Processing and Computer Vision Machine learning Multiple objective analysis Particle swarm optimization Probability and Statistics in Computer Science S.I.: From Theory to Practice: Real-World Applications of AI in Data Science Special Issue on From Theory to Practice: Real-World Applications of AI in Data Science Stability Generalization performance IMOPSO Extreme learning machine Performance stability
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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