An Analog Neural Network Implementation in Fixed Time of Adjustable-Order Statistic Filters and Applications  

Oleh:

Mestari, M.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 15 no. 3 (May 2004), page 766-785.
Topik: FILTERS; analog; neural network; implementation; fixed time; adjustable - order; statistic filters; applications

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.10 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

In this paper, we show a neural network implementation in fixed time of adjustable order statistic filters, including sorting, and adaptive - order statistic filters. All these networks accept an array of N numbers Xi = SXiMXi2EXi as input (where SXi is the sign of Xi, MXi is the mantissa normalized to m digits, and Ex is the exponent) and employ two kinds of neurons, the linear and the threshold - logic neurons, with only integer weights (most of the weights being just + 1 or - 1) and integer threshold. Therefore, this will greatly facilitate the actual hardware implementation of the proposed neural networks using currently available very large scale integration technology. An application of using minimum filter in implementing a special neural network model neural network classifier (NNC) is given. With a classification problem of l classes C1, C2,..., C1, NNC classifies in fixed time an unknown vector to one class using a minimum - distance classification technique.

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