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Neural Networks Between Integer and Rational Weights
Šíma, Jiří
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.
Fulltext: content.csg -  PDF
Plný tet: v1237-16 - PDF
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Mohou kognitivní a inteligentní systémy překonat Turingovy stroje?
Wiedermann, Jiří
We look for computational limits of artificial, natural and hybrid cognitive and intelligent systems. The common basis for such studies is offered by computationalism, i.e., the belief that cognitive or intelligent processes, respectively, are in essence computational processes. We show that in principle cognitive systems might exist whose computational power outperforms that of Turing machines and that even in practice we observe the rudiments of such systems.
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