On The Discrete-Time Dynamics of The Basic Hebbian Neural-Network Node  

Oleh:

Zufiria, P. J.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 13 no. 6 (2002), page 1342-1352.
Topik: NEURAL NETWORKS; discrete - time; basic hebbian; neural - network node

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.7A Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

In this paper, the dynamical behaviour of the basic node used for constructing Hebbian artificial neural networks (NN s) is analyzed. Hebbian NN s are employed in communications and signal processing applications, among others. They have been traditionally studied on a continuous-time formulation whose validity is justified via some analytical procedures that presume, among other hypotheses, a specific asymptotic behavior of the learning gain. The main contribution of this paper is the study of a deterministic discrete - time (DDT) formulation that characterizes the average evolution of the node, preserving the discrete - time form of the original network and gathering a more realistic behavior of the learning gain. The new deterministic discrete-time model provides some unstability results (critical for the case of large similar variance signals) which are drastically different to the ones known for the continuous-time formulation. Simulation examples support the presented results, illustrating the practical limitations of the basic Hebbian model.

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