Search results for "NEURAL NETWORK"

showing 10 items of 1385 documents

Fluctuations in mesoscopic systems

1992

Abstract Electronic wavefunctions in weakly disordered systems have been studied within the Anderson model of localization. The eigenstates calculated by means of the Lanczos diagonalization algorithm display characteristic spatial fluctuations that can be described by a multifractal analysis. For increasing disorder or energy the observed curdling of the wavefunction reflects the stronger localization, but no exponential decay can be observed. This is reflected in the set of generalized fractal dimensions and the singularity spectrum of the fractal measure.

PhysicsLanczos resamplingMesoscopic physicsFractalGeneral Chemical EngineeringQuantum mechanicsGeneral Physics and AstronomyMultifractal systemExponential decaySingularity spectrumCondensed Matter::Disordered Systems and Neural NetworksAnderson impurity modelFractal dimensionPhilosophical Magazine B
researchProduct

Noise enhanced stability in magnetic systems

2009

In this paper noise enhanced stability in magnetic systems is studied by both an Ising-type model and a Preisach–Arrhenius model as well as a dynamic Preisach model. It is shown that in one nonequilibrium Ising system noise enhanced stability occurs and that dynamic Preisach model has the capability to predict the occurrence of noise enhanced stability in magnetic systems. On the contrary, in a Preisach–Arrhenius model of a single quadrant magnetic material, noise enhanced stability is not detected.

PhysicsMagnetic noiseCondensed matter physicsIsing systemGeneral Physics and AstronomyNon-equilibrium thermodynamicsSettore ING-IND/32 - Convertitori Macchine E Azionamenti ElettriciCondensed Matter::Disordered Systems and Neural NetworksElectric Machines Power Systems Electric TechnologyMagnetizationMagnetCondensed Matter::Statistical MechanicsIsing modelStatistical physics
researchProduct

Improved Neural Networks with Random Weights for Short-Term Load Forecasting.

2015

An effective forecasting model for short-term load plays a significant role in promoting the management efficiency of an electric power system. This paper proposes a new forecasting model based on the improved neural networks with random weights (INNRW). The key is to introduce a weighting technique to the inputs of the model and use a novel neural network to forecast the daily maximum load. Eight factors are selected as the inputs. A mutual information weighting algorithm is then used to allocate different weights to the inputs. The neural networks with random weights and kernels (KNNRW) is applied to approximate the nonlinear function between the selected inputs and the daily maximum load…

PhysicsMathematical optimizationMultidisciplinaryArtificial neural networkGeneralizationlcsh:Rlcsh:MedicineA-weightingMutual informationWeightingSupport vector machineElectric power systemKernel methodElectric Power SuppliesNonlinear Dynamicslcsh:QNeural Networks Computerlcsh:ScienceAlgorithmsResearch ArticlePLoS ONE
researchProduct

Coherent potential approximation for diffusion and wave propagation in topologically disordered systems

2013

Using Gaussian integral transform techniques borrowed from functional-integral field theory and the replica trick we derive a version of the coherent-potential approximation (CPA) suited for describing ($i$) the diffusive (hopping) motion of classical particles in a random environment and ($ii$) the vibrational properties of materials with spatially fluctuating elastic coefficients in topologically disordered materials. The effective medium in the present version of the CPA is not a lattice but a homogeneous and isotropic medium, representing an amorphous material on a mesoscopic scale. The transition from a frequency-independent to a frequency-dependent diffusivity (conductivity) is shown …

PhysicsMesoscopic physicsWave propagationGaussianIsotropyFOS: Physical sciencesDisordered Systems and Neural Networks (cond-mat.dis-nn)Condensed Matter - Disordered Systems and Neural NetworksCondensed Matter PhysicsCondensed Matter::Disordered Systems and Neural NetworksElectronic Optical and Magnetic Materialssymbols.namesakeQuantum mechanicsGaussian integralsymbolsCoherent potential approximationStatistical physicsRayleigh scatteringReplica trick
researchProduct

Localization-delocalization transition for disordered cubic harmonic lattices.

2012

We study numerically the disorder-induced localization-delocalization phase transitions that occur for mass and spring constant disorder in a three-dimensional cubic lattice with harmonic couplings. We show that, while the phase diagrams exhibit regions of stable and unstable waves, the universality of the transitions is the same for mass and spring constant disorder throughout all the phase boundaries. The combined value for the critical exponent of the localization lengths of $\nu = 1.550^{+0.020}_{-0.017}$ confirms the agreement with the universality class of the standard electronic Anderson model of localization. We further support our investigation with studies of the density of states…

PhysicsModels MolecularPhase transitionCondensed matter physicsMolecular ConformationFOS: Physical sciencesDisordered Systems and Neural Networks (cond-mat.dis-nn)Condensed Matter - Disordered Systems and Neural NetworksRenormalization groupCondensed Matter PhysicsCondensed Matter::Disordered Systems and Neural NetworksPhase TransitionUniversality (dynamical systems)Models ChemicalDensity of statesGeneral Materials ScienceComputer SimulationWave functionCritical exponentAnderson impurity modelPhase diagramJournal of physics. Condensed matter : an Institute of Physics journal
researchProduct

Computer simulation of models for the structural glass transition

2008

In order to test theoretical concepts on the glass transition, we investigate several models of glassy materials by means of Monte Carlo (MC) and Molecular Dynamics (MD) computer simulations. It is shown that also simplified models exhibit a glass transition which is in qualitative agreement with experiment and that thus such models are useful to study this phenomenon. However, the glass transition temperture as well as the structural properties of the frozen-in glassy phase depend strongly on the cooling history, and the extrapolation to the limit of infinitely slow cooling velocity is nontrivial, which makes the identification of the (possible) underlying equilibrium transition very diffi…

PhysicsMolecular dynamicsSlow coolingPhase (matter)Monte Carlo methodEnthalpyExtrapolationThermodynamicsLimit (mathematics)Statistical physicsGlass transitionCondensed Matter::Disordered Systems and Neural Networks
researchProduct

Finite-size tests of hyperscaling.

1985

The possible form of hyperscaling violations in finite-size scaling theory is discussed. The implications for recent tests in Monte Carlo simulations of the d = 3 Ising model are examined, and new results for the d = 5 Ising model are presented.

PhysicsMonte Carlo methodCondensed Matter::Statistical MechanicsSquare-lattice Ising modelMonte Carlo method in statistical physicsIsing modelStatistical physicsScaling theoryCondensed Matter::Disordered Systems and Neural NetworksMonte Carlo molecular modelingPhysical review. B, Condensed matter
researchProduct

The CALMA system: an artificial neural network method for detecting masses and microcalcifications in digitized mammograms

2004

The CALMA (Computer Assisted Library for MAmmography) project is a five years plan developed in a physics research frame in collaboration between INFN (Istituto Nazionale di Fisica Nucleare) and many Italian hospitals. At present a large database of digitized mammographic images (more than 6000) was collected and a software based on neural network algorithms for the search of suspicious breast lesions was developed. Two tools are available: a microcalcification clusters hunter, based on supervised and unsupervised feedforward neural network, and a massive lesions searcher, based on a hibrid approach. Both the algorithms analyzed preprocessed digitized images by high frequency filters. Clini…

PhysicsNuclear and High Energy PhysicsArtificial neural networkmedicine.diagnostic_testbusiness.industryFrame (networking)FOS: Physical sciencesPattern recognitioncomputer.software_genreGridPhysics - Medical PhysicsSoftwareHybrid systemmedicineComputer Aided DesignFeedforward neural networkMammographyMedical Physics (physics.med-ph)Artificial intelligencebusinessInstrumentationcomputerNuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
researchProduct

Neutron detection and γ-ray suppression using artificial neural networks with the liquid scintillators BC-501A and BC-537

2019

Abstract In this work we present a comparison between the two liquid scintillators BC-501A and BC-537 in terms of their performance regarding the pulse-shape discrimination between neutrons and γ rays. Special emphasis is put on the application of artificial neural networks . The results show a systematically higher γ -ray rejection ratio for BC-501A compared to BC-537 applying the commonly used charge comparison method. Using the artificial neural network approach the discrimination quality was improved to more than 95% rejection efficiency of γ rays over the energy range 150 to 1000 keV for both BC-501A and BC-537. However, due to the larger light output of BC-501A compared to BC-537, neu…

PhysicsNuclear and High Energy PhysicsRange (particle radiation)Artificial neural network010308 nuclear & particles physicsAstrophysics::High Energy Astrophysical PhenomenaScintillator01 natural sciencesComputational physicsRecoilDeuterium0103 physical sciencesNeutron detectionNeutron010306 general physicsSpectroscopyInstrumentationNuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
researchProduct

On the correlation between phase-locking modes and Vibrational Resonance in a neuronal model

2018

International audience; We numerically and experimentally investigate the underlying mechanism leading to multiple resonances in the FitzHugh-Nagumo model driven by a bichromatic excitation. Using a FitzHugh-Nagumo circuit, we first analyze the number of spikes triggered by the system in response to a single sinusoidal wave forcing. We build an encoding diagram where different phase-locking modes are identified according to the amplitude and frequency of the sinusoidal excitation. Next, we consider the bichromatic driving which consists in a low frequency sinusoidal wave perturbed by an additive high frequency signal. Beside the classical Vibrational Resonance phenomenon, we show in real ex…

PhysicsNumerical AnalysisQuantitative Biology::Neurons and CognitionApplied MathematicsPerturbation (astronomy)phase locking modesLow frequencyneural networks01 natural sciences010305 fluids & plasmasComputational physicsCorrelationNonlinear systemnonlinear dynamicsSine waveAmplitude[NLIN.NLIN-PS]Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS]Control theoryModeling and Simulation0103 physical sciencesVibrational resonance[ NLIN.NLIN-PS ] Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS]010306 general physicsvibrational resonanceExcitation
researchProduct