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Keyword

hidden node selection

Description: The process of choosing intermediate nodes in neural networks, particularly to improve model performance in prediction and classification.
OpenAlex ID: keywords/hidden-node-selection
Enhanced random search based incremental extreme learning machine
2007 · Guang-Bin Huang, Lei Chen · Neurocomputing · 891
Tuning of the structure and parameters of a neural network using an improved genetic algorithm
2003 · F.H.F. Leung, H.K. Lam, et al. · IEEE Transactions on Neural Networks · 784
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A dynamic ensemble learning algorithm for neural networks
2019 · Kazi Md. Rokibul Alam, Nazmul Siddique, et al. · Neural Computing and Applications · 536
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A constructive algorithm for training cooperative neural network ensembles
2003 · Md.M. Islam, Xin Yao, et al. · IEEE Transactions on Neural Networks · 294
Unsupervised Learning Based On Artificial Neural Network: A Review
2018 · Happiness Ugochi Dike, Yimin Zhou, et al. · IEEE International Conference on Cyborg and Bionic Systems (CBS) · 232
Rainfall‐runoff modelling using artificial neural networks: comparison of network types
2004 · A. R. Senthil Kumar, K. P. Sudheer, et al. · Hydrological Processes · 228
Regularized extreme learning machine for regression problems
2011 · José M. Martínez-Martínez, Pablo Escandell-Montero, et al. · Neurocomputing · 213
Study of Variants of Extreme Learning Machine (ELM) Brands and its Performance Measure on Classification Algorithm
2021 · J. Samuel Manoharan · Journal of Soft Computing Paradigm · 191
Bidirectional Extreme Learning Machine for Regression Problem and Its Learning Effectiveness
2012 · Yimin Yang, Yaonan Wang, et al. · IEEE Transactions on Neural Networks and Learning Systems · 182
An integrated PSO for parameter determination and feature selection of ELM and its application in classification of power system disturbances
2015 · R. Ahila, V. Sadasivam, et al. · Applied Soft Computing · 177
Application of Genetic Algorithm Optimized Neural Network Connection Weights for Medical Diagnosis of PIMA Indians Diabetes
2011 · Asha Gowda Karegowda, A.S Manjunath, et al. · International Journal on Soft Computing · 172
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Deep neural network model with Bayesian hyperparameter optimization for prediction of NO x at transient conditions in a diesel engine
2020 · Seunghyup Shin, Youngbok Lee, et al. · Engineering Applications of Artificial Intelligence · 158
Stacked Extreme Learning Machines
2014 · Hongming Zhou, Guang-Bin Huang, et al. · IEEE Transactions on Cybernetics · 152
Rough fuzzy MLP: knowledge encoding and classification
1998 · M. Banerjee, S. Mitra, et al. · IEEE Transactions on Neural Networks · 146
Constructive hidden nodes selection of extreme learning machine for regression
2010 · Yuan Lan, Yeng Chai Soh, et al. · Neurocomputing · 140
Simulating Structural Analysis with Neural Network
1994 · James L. Rogers · Journal of Computing in Civil Engineering · 115
Parsimonious Extreme Learning Machine Using Recursive Orthogonal Least Squares
2014 · Ning Wang, Meng Joo Er, et al. · IEEE Transactions on Neural Networks and Learning Systems · 113
Estimation of the state of charge for a LFP battery using a hybrid method that combines a RBF neural network, an OLS algorithm and AGA
2013 · Wen-Yeau Chang · International Journal of Electrical Power & Energy Systems · 111
Two-stage extreme learning machine for regression
2010 · Yuan Lan, Yeng Chai Soh, et al. · Neurocomputing · 104
Recent Developments in Multilayer Perceptron Neural Networks
2005 · Walter H. Delashmit, Michael T. Manry · 103
Pressure prediction of a spark ignition single cylinder engine using optimized extreme learning machine models
2019 · Viviana Cocco Mariani, Stephan Hennings Och, et al. · Applied Energy · 97
Generalization Capability of Neural Network Models for Temperature-Frequency Correlation Using Monitoring Data
2009 · Y. Q. Ni, H. F. Zhou, et al. · Journal of Structural Engineering · 95
A new algorithm for online structure and parameter adaptation of RBF networks
2003 · Alex Alexandridis, Haralambos Sarimveis, et al. · Neural Networks · 94
Annealing robust radial basis function networks for function approximation with outliers
2003 · Chen-Chia Chuang, Jin-Tsong Jeng, et al. · Neurocomputing · 90
Optimal Design and Feature Selection by Genetic Algorithm for Emotional Artificial Neural Network (EANN) in Rainfall-Runoff Modeling
2021 · Amir Molajou, Vahid Nourani, et al. · Water Resources Management · 84
Works count: 599
Citation count: 10,160
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