A Growing and Pruning Method for Radial Basis Function Networks

@article{Bortman2009AGA,
  title={A Growing and Pruning Method for Radial Basis Function Networks},
  author={Maria Bortman and Mayer Aladjem},
  journal={IEEE Transactions on Neural Networks},
  year={2009},
  volume={20},
  pages={1039-1045}
}
A recently published generalized growing and pruning (GGAP) training algorithm for radial basis function (RBF) neural networks is studied and modified. GGAP is a resource-allocating network (RAN) algorithm, which means that a created network unit that consistently makes little contribution to the network's performance can be removed during the training. GGAP states a formula for computing the significance of the network units, which requires a d-fold numerical integration for arbitrary… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 56 CITATIONS

An Online Self-Adaption Learning Algorithm for Hyper Basis Function Neural Network

  • Chen Wu, Xian-ren Kong, Zhenguo Yang
  • Computer Science
  • 2018 2nd IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)
  • 2018
VIEW 5 EXCERPTS
CITES METHODS
HIGHLY INFLUENCED

An Incremental Design of Radial Basis Function Networks

VIEW 5 EXCERPTS
CITES METHODS
HIGHLY INFLUENCED

Real-Time Model Predictive Control Using a Self-Organizing Neural Network

VIEW 7 EXCERPTS
CITES METHODS
HIGHLY INFLUENCED

Supervisory adaptive dynamic RBF-based neural-fuzzy control system design for unknown nonlinear systems

VIEW 5 EXCERPTS
CITES BACKGROUND
HIGHLY INFLUENCED

Data-Based Predictive Control for Wastewater Treatment Process

VIEW 4 EXCERPTS
CITES METHODS & RESULTS
HIGHLY INFLUENCED

PANFIS++: A Generalized Approach to Evolving Learning

VIEW 4 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

FILTER CITATIONS BY YEAR

2009
2020

CITATION STATISTICS

  • 7 Highly Influenced Citations

References

Publications referenced by this paper.
SHOWING 1-10 OF 30 REFERENCES

Advances in Mixture Models

Unsupervised learning of Gaussian mixtures based on variational component splitting

  • A. Likas
  • IEEE Trans . Neural Netw .
  • 2007

An efficient sequential RBF network for bio-medical classification problems

An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks

Sparse modeling using orthogonal forward regression with PRESS statistic and regularization