An adaptive-PSO-based self-organizing RBF neural network
HG Han, W Lu, Y Hou, JF Qiao - IEEE transactions on neural …, 2016 - ieeexplore.ieee.org
In this paper, a self-organizing radial basis function (SORBF) neural network is designed to
improve both accuracy and parsimony with the aid of adaptive particle swarm optimization …
improve both accuracy and parsimony with the aid of adaptive particle swarm optimization …
Particle swarm optimization using dynamic tournament topology
Particle swarm optimization (PSO) is a nature-inspired global optimization method that uses
interaction between particles to find the optimal solution in a complex search space. The …
interaction between particles to find the optimal solution in a complex search space. The …
Machine learning prediction of biochar yield based on biomass characteristics
J Ma, S Zhang, X Liu, J Wang - Bioresource Technology, 2023 - Elsevier
Slow pyrolysis is a widely used thermochemical pathway that can convert organic waste into
biochar. We employed six machine learning models to predictively model 13 selected …
biochar. We employed six machine learning models to predictively model 13 selected …
Self-organizing RBF neural network using an adaptive gradient multiobjective particle swarm optimization
H Han, X Wu, L Zhang, Y Tian… - IEEE transactions on …, 2017 - ieeexplore.ieee.org
One of the major obstacles in using radial basis function (RBF) neural networks is the
convergence toward local minima instead of the global minima. For this reason, an adaptive …
convergence toward local minima instead of the global minima. For this reason, an adaptive …
Adaptive hyperparameter fine-tuning for boosting the robustness and quality of the particle swarm optimization algorithm for non-linear RBF neural network modelling …
Simple Summary A radial basis function neural network (RBFNN) is proposed for identifying
and diagnosing non-linear systems. The neural network developed was optimized not only …
and diagnosing non-linear systems. The neural network developed was optimized not only …
Fast adaptive gradient RBF networks for online learning of nonstationary time series
For a learning model to be effective in online modeling of nonstationary data, it must not only
be equipped with high adaptability to track the changing data dynamics but also maintain …
be equipped with high adaptability to track the changing data dynamics but also maintain …
FP-ELM: An online sequential learning algorithm for dealing with concept drift
D Liu, YX Wu, H Jiang - Neurocomputing, 2016 - Elsevier
The online sequential extreme learning machine (OS-ELM) algorithm is an on-line and
incremental learning method, which can learn data one-by-one or chunk-by-chunk with a …
incremental learning method, which can learn data one-by-one or chunk-by-chunk with a …
Adaptive learning for robust radial basis function networks
AK Seghouane, N Shokouhi - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
This article addresses the robust estimation of the output layer linear parameters in a radial
basis function network (RBFN). A prominent method used to estimate the output layer …
basis function network (RBFN). A prominent method used to estimate the output layer …
An RBF online learning scheme for non-stationary environments based on fuzzy means and givens rotations
Learning on non-stationary environments is laden with many challenges, as the procedure
is usually characterized by drifts and data unavailability; on the other hand, it is of great …
is usually characterized by drifts and data unavailability; on the other hand, it is of great …
A novel self-organizing TS fuzzy neural network for furnace temperature prediction in MSWI process
H He, X Meng, J Tang, J Qiao - Neural computing and applications, 2022 - Springer
In the municipal solid waste incineration (MSWI) process, it is critical to predict furnace
temperature, which is closely related to the incinerate state and the steam production, to …
temperature, which is closely related to the incinerate state and the steam production, to …