Adaptive Multilayer Perceptrons With Long- and Short-Term Memories  

Oleh:

Bassu, D. ; Lo, J. T.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 13 no. 1 (2002), page 22-33.
Topik: multilayer networks; multilayer perceptrons; memories

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.6 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

Multilayer perceptrons (MLP s) with long - and short - term memories (LASTM s) are proposed for adaptive processing. The activation functions of the output neurons of such a network are linear, and thus the weights in the last layer affect the outputs of the network linearly and are called linear weights. These linear weights constitute the short - term memory and other weights the long - term memory. It is proven that virtually any function f (x, & thetas ;) with an environmental parameter & thetas ; can be approximated to any accuracy by an MLP with LASTMs whose long - term memory is independent of & thetas ;. This independency of & thetas ; allows the long - term memory to be determined in an a priori training and allows the online adjustment of only the short - term memory for adapting to the environmental parameter & thetas ;. The benefits of using an MLP with LASTM s include less online computation, no poor local extrema to fall into, and much more timely and better adaptation. Numerical examples illustrate that these benefits are realized satisfactorily.

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