PMID- 24808504
OWN - NLM
STAT- MEDLINE
DCOM- 20150929
LR  - 20191210
IS  - 2162-2388 (Electronic)
IS  - 2162-237X (Linking)
VI  - 23
IP  - 2
DP  - 2012 Feb
TI  - Concurrent subspace width optimization method for RBF neural network modeling.
PG  - 247-59
LID - 10.1109/TNNLS.2011.2178560 [doi]
AB  - Radial basis function neural networks (RBFNNs) are widely used in nonlinear 
      function approximation. One of the challenges in RBFNN modeling is determining 
      how to effectively optimize width parameters to improve approximation accuracy. 
      To solve this problem, a width optimization method, concurrent subspace width 
      optimization (CSWO), is proposed based on a decomposition and coordination 
      strategy. This method decomposes the large-scale width optimization problem into 
      several subspace optimization (SSO) problems, each of which has a single 
      optimization variable and smaller training and validation data sets so as to 
      greatly simplify optimization complexity. These SSOs can be solved concurrently, 
      thus computational time can be effectively reduced. With top-level system 
      coordination, the optimization of SSOs can converge to a consistent optimum, 
      which is equivalent to the optimum of the original width optimization problem. 
      The proposed method is tested with four mathematical examples and one practical 
      engineering approximation problem. The results demonstrate the efficiency and 
      robustness of CSWO in optimizing width parameters over the traditional width 
      optimization methods.
FAU - Yao, Wen
AU  - Yao W
FAU - Chen, Xiaoqian
AU  - Chen X
FAU - Zhao, Yong
AU  - Zhao Y
FAU - van Tooren, Michel
AU  - van Tooren M
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - United States
TA  - IEEE Trans Neural Netw Learn Syst
JT  - IEEE transactions on neural networks and learning systems
JID - 101616214
SB  - IM
MH  - *Algorithms
MH  - *Artificial Intelligence
MH  - Computer Simulation
MH  - *Models, Statistical
MH  - *Neural Networks, Computer
MH  - *Nonlinear Dynamics
MH  - Pattern Recognition, Automated/*methods
EDAT- 2012/02/01 00:00
MHDA- 2015/09/30 06:00
CRDT- 2014/05/09 06:00
PHST- 2014/05/09 06:00 [entrez]
PHST- 2012/02/01 00:00 [pubmed]
PHST- 2015/09/30 06:00 [medline]
AID - 10.1109/TNNLS.2011.2178560 [doi]
PST - ppublish
SO  - IEEE Trans Neural Netw Learn Syst. 2012 Feb;23(2):247-59. doi: 
      10.1109/TNNLS.2011.2178560.