# The hidden neurons selection of the wavelet networks using support vector machines and ridge regression

@article{Han2008TheHN,
title={The hidden neurons selection of the wavelet networks using support vector machines and ridge regression},
author={Min Han and Jia Yin},
journal={Neurocomputing},
year={2008},
volume={72},
pages={471-479}
}
A 1-norm support vector machine stepwise (SVMS) algorithm is proposed for the hidden neurons selection of wavelet networks (WNs). In this new algorithm, the linear programming support vector machine (LPSVM) is employed to pre-select the hidden neurons, and then a stepwise selection algorithm based on ridge regression is introduced to select hidden neurons from the pre-selection. The main advantages of the new algorithm are that it can get rid of the influence of the ill conditioning of the… CONTINUE READING

#### Citations

##### Publications citing this paper.
Showing 1-10 of 13 extracted citations

## SVM-ELM: Pruning of Extreme Learning Machine with Support Vector Machines for Regression

J. Intelligent Systems • 2016
View 3 Excerpts
Highly Influenced

## Linear and non-linear proximal support vector machine classifiers for wind speed prediction

Cluster Computing • 2018
View 1 Excerpt

## Transient Clock Power Estimation of Pre-CTS Netlist

2018 IEEE International Symposium on Circuits and Systems (ISCAS) • 2018
View 1 Excerpt

## Calibrating wavelet neural networks by distance orientation similarity fuzzy C-means for approximation problems

Appl. Soft Comput. • 2016

## Comparative analysis on hidden neurons estimation in multi layer perceptron neural networks for wind speed forecasting

Artificial Intelligence Review • 2016
View 1 Excerpt

KES • 2016

## A novel criterion to select hidden neuron numbers in improved back propagation networks for wind speed forecasting

Applied Intelligence • 2015
View 1 Excerpt

## Comparison of $\ell _{1}$ -Norm SVR and Sparse Coding Algorithms for Linear Regression

IEEE Transactions on Neural Networks and Learning Systems • 2015
View 2 Excerpts

## Optimization of wavelet neural networks with the firefly algorithm for approximation problems

Neural Computing and Applications • 2015
View 1 Excerpt

## Design of wavelet neural networks based on symmetry fuzzy C-means for function approximation

Neural Computing and Applications • 2013
View 2 Excerpts

#### References

##### Publications referenced by this paper.
Showing 1-10 of 21 references

## A new class of wavelet networks for nonlinear system identification

IEEE Transactions on Neural Networks • 2005
View 7 Excerpts
Highly Influenced

## A Feature Selection Newton Method for Support Vector Machine Classification

Comp. Opt. and Appl. • 2004
View 3 Excerpts
Highly Influenced

## System identification via optimised wavelet-based neural networks

F. Alonge, F. D’Ippolito, F. M. Raimondi
IEE Proc. Control Theory Appl • 2003
View 3 Excerpts
Highly Influenced

## Exact 1-Norm Support Vector Machines Via Unconstrained Convex Differentiable Minimization

Journal of Machine Learning Research • 2006
View 2 Excerpts

View 1 Excerpt

## The Wavelet-narmax Representation: a Hybrid Model Structure Combining Polynomial Models with Multiresolution Wavelet Decompositions

Int. J. Systems Science • 2005
View 1 Excerpt

## epsilon-SSVR: A Smooth Support Vector Machine for epsilon-Insensitive Regression

IEEE Trans. Knowl. Data Eng. • 2005
View 1 Excerpt

## Backstepping wavelet neural network control for indirect field-oriented induction motor drive

IEEE Transactions on Neural Networks • 2004
View 1 Excerpt

## Term and variable selection for nonlinear system identification

H. L. Wei, S. A. Billings, J. Liu
Int. J. Control • 2004
View 2 Excerpts

## Neural-network construction and selection in nonlinear modeling

IEEE Trans. Neural Networks • 2003
View 1 Excerpt