An Analytical Framework for Local Feedforward Networks  

Oleh:

Weaver, S. ; Baird, L. ; Polycarpou, M.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 9 no. 3 (1998), page 473-482.
Topik: FRAMEWORK; analytical framework; local; networks

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.3 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

Interference in neural networks occurs when learning in one area of the input space causes unlearning in another area. Networks that are less susceptible to interference are referred to as spatially local networks. To obtain a better understanding of these properties, a theoretical framework, consisting of a measure of interference and a measure of network localization, is developed. These measures incorporate not only the network weights and architecture but also the learning algorithm. Using this framework to analyze sigmoidal, multilayer perceptron (MLP) networks that employ the backpropagation learning algorithm on the quadratic cost function, we address a familiar misconception that single - hidden - layer sigmoidal networks are inherently non local by demonstrating that given a sufficiently large number of adjustable weights, single - hidden - layer sigmoidal MLP s exist that are arbitrarily local and retain the ability to approximate any continuous function on a compact domain.

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