Publication: Dynamic self-organized learning for optimizing the complexity growth of radial basis function neural networks
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Issued Date
2002
Resource Type
File Type
application/pdf
Other identifier(s)
2-s2.0-33746931769
Rights Holder(s)
Scopus
Bibliographic Citation
Proceedings of the IEEE International Conference on Industrial Technology. Vol 1, (2002), p.655-660
Suggested Citation
Arisariyawong S., Charoenseang S. Dynamic self-organized learning for optimizing the complexity growth of radial basis function neural networks. Proceedings of the IEEE International Conference on Industrial Technology. Vol 1, (2002), p.655-660. doi:10.1109/ICIT.2002.1189980 Retrieved from: https://hdl.handle.net/20.500.14740/6808
Author(s)
Abstract
This paper proposes a framework of automatically exploring the optimal size of a radial basis function (RBF) neural network. A dynamic self-organized learning algorithm is presented to adapt the structure of the network. The algorithm generates a new hidden unit based on the steady state error of network and the nearest distance from input data to the center of hidden unit. Furthermore, it also detects and removes any insignificant contributing hidden units. For optimizing the complexity growth of RBF neural network, the growing and pruning are combined during adaptation of RBF neural network structure. The examples of nonlinear dynamical system modeling are presented to illustrate the performance of the proposed algorithm. © 2002 IEEE.
Subject(s)
Algorithms
Complex networks
Dynamical systems
Functions
Learning algorithms
Nonlinear dynamical systems
Robotics
Convergence
Function estimation
Growing and pruning
Nonlinear dynamical system modeling
Radial basis function neural networks
RBF Neural Network
Self organized learning
Steady state errors
Radial basis function networks
Complex networks
Dynamical systems
Functions
Learning algorithms
Nonlinear dynamical systems
Robotics
Convergence
Function estimation
Growing and pruning
Nonlinear dynamical system modeling
Radial basis function neural networks
RBF Neural Network
Self organized learning
Steady state errors
Radial basis function networks