939 research outputs found
Parallel dynamics of the asymmetric extremely diluted Ashkin-Teller neural network
The parallel dynamics of the asymmetric extremely diluted Ashkin-Teller
neural network is studied using signal-to-noise analysis techniques. Evolution
equations for the order parameters are derived, both at zero and finite
temperature. The retrieval properties of the network are discussed in terms of
the four-spin coupling strength and the temperature. It is shown that the
presence of a four-spin coupling enhances the retrieval quality.Comment: 6 pages, 2 eps figure
Modeling the deformation textures and microstructural evolutions of a Fe–Mn–C TWIP steel during tensile and shear testing
The high manganese austenitic steels with low stacking fault energy (SFE) present outstanding mechanical properties due to the occurrence of two strain mechanisms: dislocation glide and twinning. Both mechanisms are anisotropic. In this paper, we analyzed the effect of monotonous loading path on the texture, the deformation twinning and the stress–strain response of polycrystalline high Mn TWIP steel. Experimental data were compared to predicted results obtained by two polycrystalline models. These two models are based on the same single crystal constitutive equations but differ from the homogenization scheme. The good agreement between experiments and calculations suggest that the texture plays a key role in twinning activity and kinetics with regard to the intergranular stress heterogeneities. Rolling direction simple shear induces single twinning while rolling and transverse direction uniaxial tensions induce multi-twinning leading to lower twin volume fractions due to twin–twin interactions
Multiplicative versus additive noise in multi-state neural networks
The effects of a variable amount of random dilution of the synaptic couplings
in Q-Ising multi-state neural networks with Hebbian learning are examined. A
fraction of the couplings is explicitly allowed to be anti-Hebbian. Random
dilution represents the dying or pruning of synapses and, hence, a static
disruption of the learning process which can be considered as a form of
multiplicative noise in the learning rule. Both parallel and sequential
updating of the neurons can be treated. Symmetric dilution in the statics of
the network is studied using the mean-field theory approach of statistical
mechanics. General dilution, including asymmetric pruning of the couplings, is
examined using the generating functional (path integral) approach of disordered
systems. It is shown that random dilution acts as additive gaussian noise in
the Hebbian learning rule with a mean zero and a variance depending on the
connectivity of the network and on the symmetry. Furthermore, a scaling factor
appears that essentially measures the average amount of anti-Hebbian couplings.Comment: 15 pages, 5 figures, to appear in the proceedings of the Conference
on Noise in Complex Systems and Stochastic Dynamics II (SPIE International
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