Sparse Wavelet Networks
| Autor: | Amir Reza Sadri, Mehemmed Emre Celebi, Satish Viswanath, Nazanin Rahnavard |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Dynamical systems theory
Artificial neural network Computer science Applied Mathematics MathematicsofComputing_NUMERICALANALYSIS System identification Relaxation (iterative method) 020206 networking & telecommunications 02 engineering and technology Sparse approximation System of linear equations Wavelet Signal Processing Linear regression 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Algorithm |
| Zdroj: | IEEE Signal Processing Letters. 27:111-115 |
| ISSN: | 1558-2361 1070-9908 |
| DOI: | 10.1109/lsp.2019.2959219 |
| Popis: | A wavelet network (WN) is a feed-forward neural network that uses wavelets as activation functions for the neurons in its hidden layer. By predetermining the wavelet positions and dilations, the WN can turn into a linear regression model. The common approach for the construction of these WN families is to use least-squares type algorithms. In this letter, we propose a novel approach by formulating a WN as a sparse linear regression problem, which we call a sparse wavelet network (SWN). In this WN, the problem of calculating the unknown inner parameters of the network becomes that of finding the sparse solution of an under-determined system of linear equations. Our sparse solution algorithm is a non-convex sparse relaxation approach inspired by smoothed L0 (SL0), a distinguished sparse recovery algorithm. The proposed SWN can be applied as a tool for the prediction and identification of dynamical systems. |
| Databáze: | OpenAIRE |
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