On Neural Networks That Design Neural Associative Memories  

Oleh:

Chan, H. Y. ; Zak, S.H.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 8 no. 2 (1997), page 360-372.
Topik: DESIGN; neural networks; design; neural associative; memory

Ketersediaan

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

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Isi artikel

The design problem of generalized brain -state - in - a - box (GBSB) type associative memories is formulated as a constrained optimization program, and “designer” neural networks for solving the program in real time are proposed. The stability of the designer networks is analyzed using Barbalat's lemma. The analyzed and synthesized neural associative memories do not require symmetric weight matrices. Two types of the GBSB - based associative memories are analyzed, one when the network trajectories are constrained to reside in the hypercube [-1, 1]n and the other type when the network trajectories are confined to stay in the hypercube [0, 1]n. Numerical examples and simulations are presented to illustrate the results obtained.

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