Original title: Neural Networks Between Integer and Rational Weights
Authors: Šíma, Jiří
Document type: Research reports
Year: 2016
Language: eng
Series: Technical Report, volume: V-1237
Abstract: The analysis of the computational power of neural networks with the weight parameters between integer and rational numbers is refined. We study an intermediate model of binary-state neural networks with integer weights, corresponding to finite automata, which is extended with an extra analog unit with rational weights, as already two additional analog units allow for Turing universality. We characterize the languages that are accepted by this model in terms of so-called cut languages which are combined in a certain way by usual string operations. We employ this characterization for proving that the languages accepted by neural networks with an analog unit are context-sensitive and we present an explicit example of such non-context-free languages. In addition, we formulate a sufficient condition when these networks accept only regular languages in terms of quasi-periodicity of parameters derived from their weights.
Keywords: analog unit; computational power; cut languages; neural networks; rational weight
Project no.: GBP202/12/G061 (CEP)
Funding provider: GA ČR
Rights: This work is protected under the Copyright Act No. 121/2000 Coll.

Institution: Institute of Computer Science AS ČR (web)
Original record: http://hdl.handle.net/11104/0264100

Permalink: http://www.nusl.cz/ntk/nusl-261501


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