Capabilities of A Four-Layered Feedforward Neural : Four Layers Versus Three  

Oleh:

Tateishi, M. ; Tamura, S.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 8 no. 2 (1997), page 251-255.
Topik: CAPABILITIES; capabilities; four - layered; neural; layers

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.2 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

Neural-network theorems state that only when there are infinitely many hidden units is a four - layered feedforward neural network equivalent to a three - layered feedforward neural network. In actual applications, however, the use of infinitely many hidden units is impractical. Therefore, studies should focus on the capabilities of a neural network with a finite number of hidden units, In this paper, a proof is given showing that a three - layered feedforward network with N - 1 hidden units can give any N input - target relations exactly. Based on results of the proof, a four - layered network is constructed and is found to give any N input-target relations with a negligibly small error using only (N / 2) + 3 hidden units. This shows that a four - layered feedforward network is superior to a three - layered feedforward network in terms of the number of parameters needed for the training data.

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