Search results for "NEURAL NETWORK"

showing 10 items of 1385 documents

Identification of the Parameters of Reduced Vector Preisach Model by Neural Networks

2008

This paper presents a methodology for identifying reduced vector Preisach model parameters by using neural networks. The neural network used is a multiplayer perceptron trained with the Levenberg-Marquadt training algorithm. The network is trained by some hysteresis data, which are generated by using reduced vector Preisach model with preassigned parameters. It is shown how a properly trained network is able to find the parameters needed to best fit a magnetization hysteresis curve.

Artificial neural networkEstimation theoryComputer sciencebusiness.industryDifferential equationComputer Science::Neural and Evolutionary ComputationPattern recognitionMagnetic hysteresisPerceptronMagnetic susceptibilityElectronic Optical and Magnetic MaterialsIdentification (information)MagnetizationHysteresisMultilayer perceptronArtificial intelligenceElectrical and Electronic EngineeringbusinessSaturation (magnetic)
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Face tracking and recognition: from algorithm to implementation

2002

This paper describes a system capable of realizing a face detection and tracking in video sequences. In developing this system, we have used a RBF neural network to locate and categorize faces of different dimensions. The face tracker can be applied to a video communication system which allows the users to move freely in front of the camera while communicating. The system works at several stages. At first, we extract useful parameters by a low-pass filtering to compress data and we compose our codebook vectors. Then, the RBF neural network realizes a face detection and tracking on a specific board.

Artificial neural networkFacial motion captureComputer sciencebusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONCodebookTracking (particle physics)Facial recognition systemObject-class detectionVideo trackingComputer visionArtificial intelligenceFace detectionbusinessSPIE Proceedings
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Has financial attitude impacted the trading activity of retail investors during the COVID-19 pandemic?

2020

Financial attitude influences the financial behavior of retail investors. Although the extant research has acknowledged and examined this relationship, the measures of financial attitude and behavior still vary widely and are generally posed as a series of questions rather than statements. In addition to this, there is insufficient knowledge regarding retail investors' behavior in the face of a health crisis, such as the current COVID-19 pandemic. This study addresses these gaps in the prior literature by examining the relative influence of six dimensions of financial attitude, namely, financial anxiety, optimism, financial security, deliberative thinking, interest in financial issues, and …

Artificial neural networkFinancial behaviorCoronavirus disease 2019 (COVID-19)media_common.quotation_subjectFinancial anxietyFace (sociological concept)ArticleOptimismPrecautionary savings0502 economics and businessPandemicmedicineFinancial securitymedia_commonFinanceMarketingPandemicbusiness.industry:Samfunnsvitenskap: 200 [VDP]05 social sciencesCOVID-19Peer reviewAnxietyVDP::Samfunnsvitenskap: 200050211 marketingBusinessmedicine.symptomFinancial attitude050203 business & managementJournal of Retailing and Consumer Services
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Hybrid architecture for shape reconstruction and object recognition

1998

The proposed architecture is aimed to recover 3-D- shape information from gray-level images of a scene; to build a geometric representation of the scene in terms of geometric primitives; and to reason about the scene. The novelty of the architecture is in fact the integration of different approaches: symbolic reasoning techniques typical of knowledge representation in artificial intelligence, algorithmic capabilities typical of artificial vision schemes, and analogue techniques typical of artificial neural networks. Experimental results obtained by means of an implemented version of the proposed architecture acting on real scene images are reported to illustrate the system capabilities.

Artificial neural networkKnowledge representation and reasoningComputer sciencebusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONCognitive neuroscience of visual object recognitionImage processingTheoretical Computer ScienceHuman-Computer InteractionArtificial IntelligenceComputer Science::Computer Vision and Pattern RecognitionPattern recognition (psychology)Systems architectureComputer visionGeometric primitiveArtificial intelligenceGraphicsbusinessSoftware
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Neural Networks with Multidimensional Cross-Entropy Loss Functions

2019

Deep neural networks have emerged as an effective machine learning tool successfully applied for many tasks, such as misinformation detection, natural language processing, image recognition, machine translation, etc. Neural networks are often applied to binary or multi-class classification problems. In these settings, cross-entropy is used as a loss function for neural network training. In this short note, we propose an extension of the concept of cross-entropy, referred to as multidimensional cross-entropy, and its application as a loss function for classification using neural networks. The presented computational experiments on a benchmark dataset suggest that the proposed approaches may …

Artificial neural networkMachine translationbusiness.industryComputer scienceBinary number02 engineering and technologyFunction (mathematics)Extension (predicate logic)010502 geochemistry & geophysicsMachine learningcomputer.software_genre01 natural sciencesComputingMethodologies_PATTERNRECOGNITIONCross entropy020401 chemical engineeringBenchmark (computing)Deep neural networksArtificial intelligence0204 chemical engineeringbusinesscomputer0105 earth and related environmental sciences
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A Neural Solution for a Mobile Robot Navigation into Unknown Indoor Environments Using Visual Landmarks

1998

In this paper we present a neural solution for a mobile robot navigation into unknown indoor environments by using landmarks. Robot navigation task is implemented by two groups of processes based on MLP neural networks classifiers: a Low Level Vision System performs obstacle avoidance and corridor following, while an High Level Vision System extracts landmarks contents and performs goal directed navigation. A path-planner manages the two navigation systems and interacts with the robot hardware. The proposed solution is very strong and flexible and can be used to drive a mobile robot in real indoor environments. In the paper experimental results are also reported.

Artificial neural networkMachine visionComputer sciencebusiness.industryObstacle avoidanceRobotComputer visionMobile robotArtificial intelligenceVisual landmarksbusinessMobile robot navigationTask (project management)
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Global exponential stability of delayed Markovian jump fuzzy cellular neural networks with generally incomplete transition probability

2014

The problem of global exponential stability in mean square of delayed Markovian jump fuzzy cellular neural networks (DMJFCNNs) with generally uncertain transition rates (GUTRs) is investigated in this paper. In this GUTR neural network model, each transition rate can be completely unknown or only its estimate value is known. This new uncertain model is more general than the existing ones. By constructing suitable Lyapunov functionals, several sufficient conditions on the exponential stability in mean square of its equilibrium solution are derived in terms of linear matrix inequalities (LMIs). Finally, a numerical example is presented to illustrate the effectiveness and efficiency of our res…

Artificial neural networkMarkov chainCognitive NeuroscienceTransition rate matrixMarkov ChainsMarkovian jumpLyapunov functionalExponential stabilityArtificial IntelligenceControl theoryFuzzy cellular neural networksApplied mathematicsNeural Networks ComputerEquilibrium solutionAlgorithmsMathematicsNeural Networks
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Web Usage Mining by Neural Hybrid Prediction with Markov Chain Components

2021

This paper presents and evaluates a two-level web usage prediction technique, consisting of a neural network in the first level and contextual component predictors in the second level. We used Markov chains of different orders as contextual predictors to anticipate the next web access based on specific web access history. The role of the neural network is to decide, based on previous behaviour, whose predictor’s output to use. The predicted web resources are then prefetched into the cache of the browser. In this way, we considerably increase the hit rate of the web browser, which shortens the load times. We have determined the optimal configuration of the proposed hybrid predictor on a real…

Artificial neural networkMarkov chainComputer Networks and CommunicationsComputer scienceWeb prefetchingcomputer.software_genreWeb miningComponent (UML)Hit rateCacheData miningWeb resourcecomputerSoftwareInformation SystemsJournal of Web Engineering
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A COMPARATIVE STUDY OF PHENOMENOLOGICAL MODELS OF MR BRAKE BASED ON NEURAL NETWORKS APPROACH

2013

In this paper a full-scale commercially available magnetorheological (MR) brake installed in a semi-active suspension (SAS) system is modeled and simulated. Two well-known phenomenological hysteresis models are explored: Bouc–Wen and Dahl ones. In particular, influence of their parameters on the response is evaluated and assessed. The next step is to introduce the artificial neural networks and discuss their application in the field of systems identification. Subsequently, two feedforward neural networks are created and trained to estimate parameters characterizing each of the MR damper models described. The semi-active suspension (SAS) system equipped with a MR brake is described and the …

Artificial neural networkMathematical modelComputer scienceControl theoryApplied MathematicsSignal ProcessingBrakeReference data (financial markets)Magnetorheological fluidExperimental dataFeedforward neural networkInformation SystemsDamperInternational Journal of Wavelets, Multiresolution and Information Processing
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Ventricular fibrillation detection from ECG surface electrodes using different filtering techniques, window length and artificial neural networks

2017

Medical personnel face many difficulties when diagnosing ventricular fibrillation (VF). Its correct diagnosis allows to decide the right medical treatment and, therefore, it is essential to tell it apart adequately from ventricular tachycardia (VT) and other arrhythmias. If the required therapy is not appropriate, the personnel could cause serious injuries or even induce VF. In this work, a diagnosis automatic system for the detection of VF through feature extraction was developed. To verify the validity of this method, an Artificial Neural Network (ANN) classifier was used. The ECG signals used were obtained from the MIT-BIH Malignant Ventricular Arrhythmia Database and AHA (2000 series) d…

Artificial neural networkMedical treatmentmedicine.diagnostic_testComputer sciencebusiness.industryFeature extractionPattern recognitionmedicine.diseaseVentricular tachycardiaVentricular fibrillationmedicineArtificial intelligenceEcg signalbusinessElectrocardiographyClassifier (UML)2017 International Conference on Emerging Trends in Computing and Communication Technologies (ICETCCT)
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