891 resultados para Intrinsic subspace convergence
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When Recurrent Neural Networks (RNN) are going to be used as Pattern Recognition systems, the problem to be considered is how to impose prescribed prototype vectors ξ^1,ξ^2,...,ξ^p as fixed points. The synaptic matrix W should be interpreted as a sort of sign correlation matrix of the prototypes, In the classical approach. The weak point in this approach, comes from the fact that it does not have the appropriate tools to deal efficiently with the correlation between the state vectors and the prototype vectors The capacity of the net is very poor because one can only know if one given vector is adequately correlated with the prototypes or not and we are not able to know what its exact correlation degree. The interest of our approach lies precisely in the fact that it provides these tools. In this paper, a geometrical vision of the dynamic of states is explained. A fixed point is viewed as a point in the Euclidean plane R2. The retrieving procedure is analyzed trough statistical frequency distribution of the prototypes. The capacity of the net is improved and the spurious states are reduced. In order to clarify and corroborate the theoretical results, together with the formal theory, an application is presented
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This article presents the principal results of the doctoral thesis “Isomerism as internal symmetry of molecules” by Valentin Vankov Iliev (Institute of Mathematics and Informatics), successfully defended before the Specialised Academic Council for Informatics and Mathematical Modelling on 15 December, 2008.
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This paper is partially supported by project ISM-4 of Department for Scientific Research, “Paisii Hilendarski” University of Plovdiv.
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In this paper, we give a criterion for unconditional convergence with respect to some summability methods, dealing with the topological size of the set of choices of sign providing convergence. We obtain similar results for boundedness. In particular, quasi-sure unconditional convergence implies unconditional convergence.
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* This work was supported by National Science Foundation grant DMS 9404431.
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2000 Mathematics Subject Classification: 44A15, 44A35, 46E30
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A new type of fibre-optic biochemical concentration sensor based on a polymer optical fibre Bragg grating (POFBG) is proposed. The wavelength of the POFBG varies as a function of analyte concentration. The feasibility of this sensing concept is demonstrated by a saline concentration sensor. When polymer fibre is placed in a water based solution the process of osmosis takes place in this water-fibre system. An osmotic pressure which is proportional to the solution concentration, will apply to the fibre in addition to the hydraulic pressure. It tends to drive the water content out of the fibre and into the surrounding solution. When the surrounding solution concentration increases the osmotic pressure increases to drive the water content out of the fibre, consequently increasing the differential hydraulic pressure and reducing the POFBG wavelength. This process will stop once there is a balance between the osmotic pressure and the differential hydraulic pressure. Similarly when the solution concentration decreases the osmotic pressure decreases, leading to a dominant differential hydraulic pressure which drives the water into the fibre till a new pressure balance is established. Therefore the water content in the polymer fibre - and consequently the POFBG wavelength - depends directly on the solution concentration. A POFBG wavelength change of 0.9 nm was measured for saline concentration varying from 0 to 22%. For a wavelength interrogation system with a resolution of 1 pm, a measurement of solution concentration of 0.03% can be expected.
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Mathematics Subject Classification: 30B10, 30B30; 33C10, 33C20
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In this work we give su±cient conditions for k-th approximations of the polynomial roots of f(x) when the Maehly{Aberth{Ehrlich, Werner-Borsch-Supan, Tanabe, Improved Borsch-Supan iteration methods fail on the next step. For these methods all non-attractive sets are found. This is a subsequent improvement of previously developed techniques and known facts. The users of these methods can use the results presented here for software implementation in Distributed Applications and Simulation Environ- ments. Numerical examples with graphics are shown.
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We present quasi-Monte Carlo analogs of Monte Carlo methods for some linear algebra problems: solving systems of linear equations, computing extreme eigenvalues, and matrix inversion. Reformulating the problems as solving integral equations with a special kernels and domains permits us to analyze the quasi-Monte Carlo methods with bounds from numerical integration. Standard Monte Carlo methods for integration provide a convergence rate of O(N^(−1/2)) using N samples. Quasi-Monte Carlo methods use quasirandom sequences with the resulting convergence rate for numerical integration as good as O((logN)^k)N^(−1)). We have shown theoretically and through numerical tests that the use of quasirandom sequences improves both the magnitude of the error and the convergence rate of the considered Monte Carlo methods. We also analyze the complexity of considered quasi-Monte Carlo algorithms and compare them to the complexity of the analogous Monte Carlo and deterministic algorithms.
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2000 Mathematics Subject Classification: 47H04, 65K10.
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2000 Mathematics Subject Classification: 41A05.
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AMS subject classification: 65J15, 47H04, 90C30.
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AMS subject classification: 49N35,49N55,65Lxx.