Search results for " network model"

showing 10 items of 37 documents

Analysis and simulation of creativity learning by means of artificial neural networks

2007

The paper presents a new neural network approach for analysis and simulation of creative behavior. The used concept of Dynamically Controlled Neural Gas (DyCoNG) entails a combination of Dynamically Controlled Network [Perl, J. (2004a). A neural network approach to movement pattern analysis. Human Movement Science,23, 605-620] and Growing Neural Gas (Fritzke, 1995) by quality neurons. A quality neuron reflects the rareness of a piece of information and therefore can measure the originality of a recorded activity that was assigned to the neuron during the network training. The DyCoNG approach was validated using data from a longitudinal field-based study. The creative behavior of 42 particip…

Neural gasProcess (engineering)media_common.quotation_subjectBiophysicsExperimental and Cognitive PsychologyMachine learningcomputer.software_genreNetwork simulationCreativityArtificial IntelligenceHumansLearningComputer SimulationOrthopedics and Sports Medicinecomputer.programming_languagemedia_commonArtificial neural networkbusiness.industryGeneral MedicineCreativityPattern recognition (psychology)Neural Networks ComputerArtificial intelligencePerlbusinessPsychologycomputerNervous system network modelsHuman Movement Science
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Dynamic Pattern Recognition in Sport by Means of Artificial Neural Networks

2008

Behavioural processes like those in sports, motor activities or rehabilitation are often the object of optimization methods. Such processes are often characterized by a complex structure. Measurements considering them may produce a huge amount of data. It is an interesting challenge not only to store these data, but also to transform them into useful information. Artificial Neural Networks turn out to be an appropriate tool to transform abstract numbers into informative patterns that help to understand complex behavioural phenomena. The contribution presents some basic ideas of neural network approaches and several examples of application. The aim is to give an impression of how neural meth…

Physical neural networkArtificial Intelligence Systembusiness.industryTime delay neural networkComputer scienceDeep learningNeocognitronMachine learningcomputer.software_genreCellular neural networkArtificial intelligenceTypes of artificial neural networksbusinesscomputerNervous system network models
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Evidence against a glass transition in the 10-state short range Potts glass

2002

We present the results of Monte Carlo simulations of two different 10-state Potts glasses with random nearest neighbor interactions on a simple cubic lattice. In the first model the interactions come from a \pm J distribution and in the second model from a Gaussian one, and in both cases the first two moments of the distribution are chosen to be equal to J_0=-1 and Delta J=1. At low temperatures the spin autocorrelation function for the \pm J model relaxes in several steps whereas the one for the Gaussian model shows only one. In both systems the relaxation time increases like an Arrhenius law. Unlike the infinite range model, there are only very weak finite size effects and there is no evi…

PhysicsArrhenius equationStatistical Mechanics (cond-mat.stat-mech)GaussianMonte Carlo methodAutocorrelationFOS: Physical sciencesGeneral Physics and AstronomyDisordered Systems and Neural Networks (cond-mat.dis-nn)Condensed Matter - Disordered Systems and Neural Networksk-nearest neighbors algorithmsymbols.namesakesymbolsStatistical physicsGlass transitionGaussian network modelCondensed Matter - Statistical MechanicsSpin-½
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Drift-controlled anomalous diffusion: a solvable Gaussian model

2000

We introduce a Langevin equation characterized by a time dependent drift. By assuming a temporal power-law dependence of the drift we show that a great variety of behavior is observed in the dynamics of the variance of the process. In particular diffusive, subdiffusive, superdiffusive and stretched exponentially diffusive processes are described by this model for specific values of the two control parameters. The model is also investigated in the presence of an external harmonic potential. We prove that the relaxation to the stationary solution is power-law in time with an exponent controlled by one of model parameters.

PhysicsStatistical Mechanics (cond-mat.stat-mech)Stochastic processAnomalous diffusionFOS: Physical sciencesLangevin equationsymbols.namesakeExponential growthExponentsymbolsRelaxation (physics)Statistical physicsGaussian network modelBrownian motionCondensed Matter - Statistical MechanicsPhysical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics
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On Inverse Distance Weighting in Pollution Models

2011

When evaluating the impact of pollution, measurements from remote stations are often weighted by the inverse of distance raised to some nonnegative power (IDW). This is derived from Shepard's method of spatial interpolation (1968). The paper discusses the arbitrary character of the exponent of distance and the problem of monitoring stations that are close to the reference point. From elementary laws of physics, it is determined which exponent of distance should be chosen (or its upper bound) depending on the form of pollution encountered, such as radiant pollution (including radioactivity and sound), air pollution (plumes, puffs, and motionless clouds by using the classical Gaussian model),…

PollutionMeteorologymedia_common.quotation_subjectAir pollutionmedicine.disease_causeUpper and lower boundsWeightingMultivariate interpolationsymbols.namesakeInverse distance weightingsymbolsExponentmedicineEnvironmental scienceGaussian network modelPhysics::Atmospheric and Oceanic Physicsmedia_commonSSRN Electronic Journal
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Pollution models and inverse distance weighting: some critical remarks

2013

International audience; When evaluating the impact of pollution, measurements from remote stations are often weighted by the inverse of distance raised to some nonnegative power (IDW). This is derived from Shepard's method of spatial interpolation (1968). The paper discusses the arbitrary character of the exponent of distance and the problem of monitoring stations that are close to the reference point. From elementary laws of physics, it is determined which exponent of distance should be chosen (or its upper bound) depending on the form of pollution encountered, such as radiant pollution (including radioactivity and sound), air pollution (plumes, puffs, and motionless clouds by using the cl…

PollutionMeteorologymedia_common.quotation_subjectAir pollutionmedicine.disease_causeWeightingdistance inverseUpper and lower boundsMultivariate interpolationsymbols.namesakeInverse distance weightingStatisticsmedicineIDW[ SHS.ECO ] Humanities and Social Sciences/Economies and financesComputers in Earth Sciences[SHS.ECO] Humanities and Social Sciences/Economics and FinancePhysics::Atmospheric and Oceanic Physicsmedia_commonMathematicsExponentexposant[SHS.ECO]Humanities and Social Sciences/Economics and Finance[SDE.ES]Environmental Sciences/Environmental and SocietyPollutionWeightingpondérationExponentsymbolsShepard[SDE.ES] Environmental Sciences/Environmental and SocietyGaussian network modelInverse distance[ SDE.ES ] Environmental Sciences/Environmental and SocietyInformation Systems
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Neural networks for animal science applications: Two case studies

2006

Abstract Artificial neural networks have shown to be a powerful tool for system modelling in a wide range of applications. In this paper, we focus on neural network applications to intelligent data analysis in the field of animal science. Two classical applications of neural networks are proposed: time series prediction and clustering. The first task is related to the prediction of weekly milk production in goat flocks, which includes a knowledge discovery stage in order to analyse the relative relevance of the different variables. The second task is the clustering of goat flocks; it is used to analyse different livestock surveys by using self-organizing maps and the adaptive resonance theo…

Self-organizing mapArtificial neural networkbusiness.industryComputer scienceTime delay neural networkDeep learningGeneral EngineeringMachine learningcomputer.software_genreComputer Science ApplicationsProbabilistic neural networkAdaptive resonance theoryAnimal scienceArtificial IntelligenceMultilayer perceptronCellular neural networkArtificial intelligenceData miningTypes of artificial neural networksbusinessCluster analysiscomputerNervous system network modelsExpert Systems with Applications
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A model of the filling process of an intermittent distribution network

2010

In many countries, private tanks are acquired by users to reduce their vulnerability to intermittent supply. The presence of these local reservoirs modifies the user demand pattern and usually increases user water demand at the beginning of the service period depending on the tank filling process. This practice is thus responsible for the inequality that occurs among users: those located in advantaged positions of the network are able to obtain water resources soon after the service period begins, while disadvantaged users have to wait much longer, after the network is full. This dynamic process requires the development of ad hoc models in order to obtain reliable results. This paper discus…

Service (systems architecture)Distribution networksComputer simulationOperations researchComputer scienceProcess (engineering)Settore ICAR/02 - Costruzioni Idrauliche E Marittime E IdrologiaGeography Planning and DevelopmentEnvironmental engineeringpipe filling proceWater demandWater resourcesOrder (business)private water tankintermittent distributionwater distribution network modellingWater Science and TechnologyVulnerability (computing)
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A performance based approach for the analysis of urban water distribution systems under drought conditions

2009

The present study proposes some performance indicators for analysing urban water distribution system characterised by intermittent supply service. Specific indicators have been proposed for investigating the reduction of quality of water service under resources scarcity conditions. A modelling procedure has been implemented in order to allow for analysing complex network schemes in which users self adapt to scarcity conditions. The procedure has been applied to analyse the performance of an existing supply system in Palermo (Italy). The network and users’ behaviour in intermittent distribution conditions have been monitored in order to calibrate the model and verify the reliability of the p…

Settore ICAR/02 - Costruzioni Idrauliche E Marittime E Idrologiaperformance indicators water scarcity network models
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Energy balance in single exposure multispectral sensors

2013

International audience; Recent simulations of multispectral sensors are based on a simple Gaussian model, which includes filters transmittance and substrate absorption. In this paper we want to make the distinction between these two layers. We discuss the balance of energy by channel in multispectral solid state sensors and propose an updated simple Gaussian model to simulate multispectral sensors. Results are based on simulation of typical sensor configurations.

SiliconMaterials science[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processingoptical sensorsChannel (digital image)Equations[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingMultispectral imageComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONPhotodetectorGaussian processes02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing01 natural sciences010309 opticssymbols.namesakeMathematical model[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing0103 physical sciences0202 electrical engineering electronic engineering information engineeringTransmittanceComputer Science::Networking and Internet ArchitectureSpectral and color filter arraysoptical filtersOptical filterGaussian processPhysics::Atmospheric and Oceanic Physics[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingRemote sensingtransmittance filterSubstratesSensorsGaussian modelmultispectral solid state sensorCamerasenergy balancespectral analysisConvolutionexposure multispectral sensorComputer Science::Computer Vision and Pattern Recognitionsubstrate absorptionlight absorptionlight sensorsymbolstransmittance filters020201 artificial intelligence & image processingGaussian network model[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingEnergy (signal processing)
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