IEEE Trans Neural Netw Learn Syst - An incremental design of radial basis function networks.

Tópicos

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{ error(1145) method(1030) estim(1020) }
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{ imag(2830) propos(1344) filter(1198) }
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{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

This paper proposes an offline algorithm for incrementally constructing and training radial basis function (RBF) networks. In each iteration of the error correction (ErrCor) algorithm, one RBF unit is added to fit and then eliminate the highest peak (or lowest valley) in the error surface. This process is repeated until a desired error level is reached. Experimental results on real world data sets show that the ErrCor algorithm designs very compact RBF networks compared with the other investigated algorithms. Several benchmark tests such as the duplicate patterns test and the two spiral problem were applied to show the robustness of the ErrCor algorithm. The proposed ErrCor algorithm generates very compact networks. This compactness leads to greatly reduced computation times of trained networks.

Resumo Limpo

paper propos offlin algorithm increment construct train radial basi function rbf network iter error correct errcor algorithm one rbf unit ad fit elimin highest peak lowest valley error surfac process repeat desir error level reach experiment result real world data set show errcor algorithm design compact rbf network compar investig algorithm sever benchmark test duplic pattern test two spiral problem appli show robust errcor algorithm propos errcor algorithm generat compact network compact lead great reduc comput time train network

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