@article{Sadri2020,
   abstract = {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.},
   author = {A.R. Sadri and M.E. Celebi and N. Rahnavard and S.E. Viswanath},
   doi = {10.1109/LSP.2019.2959219},
   issn = {15582361},
   journal = {IEEE Signal Processing Letters},
   keywords = {Wavelet network,non-convex regularization,sparse representation,system identification},
   title = {Sparse Wavelet Networks},
   volume = {27},
   year = {2020},
}

{"_id":"uFtqd2BEFyyhwBpDg","bibbaseid":"sadri-celebi-rahnavard-viswanath-sparsewaveletnetworks-2020","authorIDs":["4Bygm8ju5EJL5ymrQ","6QhxQnZANogYLvQK3","95yz33vzsQF4xkftF","9XZAWfqroLnhv8Yvi","FNZqkqoLDpk7PuWdD","MFZedjHxD2knXtEDJ","QcPeEw2JciXtbZFYv","h2B3dsR3oRk42DaEg","ikoetnssERNBFB49Z","kEjtqmTXpJD7idBiM","mKXojsM3x8movswn3","mTxR5EmKaPZT4FPRw","wqGyw5WWC92w63FBr","zmtHQr2MyDkT4KaPS","zy5Yt2FBfMrjEQLRW"],"author_short":["Sadri, A.","Celebi, M.","Rahnavard, N.","Viswanath, S."],"bibdata":{"bibtype":"article","type":"article","abstract":"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.","author":[{"firstnames":["A.R."],"propositions":[],"lastnames":["Sadri"],"suffixes":[]},{"firstnames":["M.E."],"propositions":[],"lastnames":["Celebi"],"suffixes":[]},{"firstnames":["N."],"propositions":[],"lastnames":["Rahnavard"],"suffixes":[]},{"firstnames":["S.E."],"propositions":[],"lastnames":["Viswanath"],"suffixes":[]}],"doi":"10.1109/LSP.2019.2959219","issn":"15582361","journal":"IEEE Signal Processing Letters","keywords":"Wavelet network,non-convex regularization,sparse representation,system identification","title":"Sparse Wavelet Networks","volume":"27","year":"2020","bibtex":"@article{Sadri2020,\n abstract = {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.},\n author = {A.R. Sadri and M.E. Celebi and N. Rahnavard and S.E. Viswanath},\n doi = {10.1109/LSP.2019.2959219},\n issn = {15582361},\n journal = {IEEE Signal Processing Letters},\n keywords = {Wavelet network,non-convex regularization,sparse representation,system identification},\n title = {Sparse Wavelet Networks},\n volume = {27},\n year = {2020},\n}\n","author_short":["Sadri, A.","Celebi, M.","Rahnavard, N.","Viswanath, S."],"key":"Sadri2020","id":"Sadri2020","bibbaseid":"sadri-celebi-rahnavard-viswanath-sparsewaveletnetworks-2020","role":"author","urls":{},"keyword":["Wavelet network","non-convex regularization","sparse representation","system identification"],"metadata":{"authorlinks":{"viswanath, s":"https://bibbase.org/service/mendeley/eaba325f-653b-3ee2-b960-0abd5146933e","viswanath, s":"https://case.edu/engineering/labs/invent/publications"}},"downloads":0},"bibtype":"article","creationDate":"2020-04-20T04:32:18.576Z","downloads":0,"keywords":["wavelet network","non-convex regularization","sparse representation","system identification"],"search_terms":["sparse","wavelet","networks","sadri","celebi","rahnavard","viswanath"],"title":"Sparse Wavelet Networks","year":2020,"biburl":"https://bibbase.org/f/w8Gpb3sdkT5KfcMbC/export.bib","dataSources":["EqsrWP7MGiqJ6MAPy","ya2CyA73rpZseyrZ8","MFTLffJ4nzQec8k8n","2252seNhipfTmjEBQ","xGcAuoNZfYSSjMypc"]}