Encoding Strategy for Maximum Noise Tolerance Bidirectional Associative Memory  

Oleh:

Shen, D. ; Cruz, J. B., [Jr.]

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 16 no. 2 (Mar. 2005), page 293-300.
Topik: MEMORY; bidirectional associative memory (BAM); energy well hyper - radius; neural network training; noise tolerance set

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.12 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

In this paper, the basic bidirectional associative memory (BAM) is extended by choosing weights in the correlation matrix, for a given set of training pairs, which result in a maximum noise tolerance set for BAM. We prove that for a given set of training pairs, the maximum noise tolerance set is the largest, in the sense that this optimized BAM will recall the correct training pair if any input pattern is within the maximum noise tolerance set and at least one pattern outside the maximum noise tolerance set by one Hamming distance will not converge to the correct training pair. This maximum tolerance set is the union of the maximum basins of attraction. A standard genetic algorithm (GA) is used to calculate the weights to maximize the objective function which generates a maximum tolerance set for BAM. Computer simulations are presented to illustrate the error correction and fault tolerance properties of the optimized BAM.

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