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Publication DateIEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, 04/2016, Volume 27, Issue 4, pp. 809 - 821
Extreme learning machine (ELM) is an emerging learning algorithm for the generalized single hidden layer feedforward neural networks, of which the hidden node...
Training | multilayer perceptron (MLP) | random feature mapping | Least squares approximations | Artificial neural networks | Feature extraction | Nonhomogeneous media | Deep learning (DL) | deep neural network (DNN) | extreme learning machine (ELM) | Optimization | Extreme learning machine (ELM) | Multilayer perceptron (MLP) | Deep neural network (DNN) | COMPUTER SCIENCE, HARDWARE & ARCHITECTURE | HIDDEN NODES | RECOGNITION | ALGORITHM | REPRESENTATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | FEEDFORWARD NETWORKS | COMPUTER SCIENCE, THEORY & METHODS | Neural networks
Training | multilayer perceptron (MLP) | random feature mapping | Least squares approximations | Artificial neural networks | Feature extraction | Nonhomogeneous media | Deep learning (DL) | deep neural network (DNN) | extreme learning machine (ELM) | Optimization | Extreme learning machine (ELM) | Multilayer perceptron (MLP) | Deep neural network (DNN) | COMPUTER SCIENCE, HARDWARE & ARCHITECTURE | HIDDEN NODES | RECOGNITION | ALGORITHM | REPRESENTATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | FEEDFORWARD NETWORKS | COMPUTER SCIENCE, THEORY & METHODS | Neural networks
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Loading RightsInternational Communications in Heat and Mass Transfer, ISSN 0735-1933, 10/2015, Volume 67, pp. 46 - 50
This paper presents an investigation on the thermal conductivity of nanofluids using experimental data, neural networks, and correlation for modeling thermal...
Thermal conductivity | Nanofluids | Artificial neural network
Thermal conductivity | Nanofluids | Artificial neural network
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Loading RightsEnergy Conversion and Management, ISSN 0196-8904, 05/2014, Volume 81, pp. 1 - 9
Wind speed knowledge is prerequisite in the siting of wind turbines. In consequence the wind energy use requires meticulous and specified knowledge of the wind...
Multilayer perceptron | Artificial neural network | Wind speed | Forecasting | SPATIAL ESTIMATION | MECHANICS | THERMODYNAMICS | ENERGY & FUELS | FORECASTING WIND | ARTIFICIAL NEURAL-NETWORKS | PREDICTION | Air-turbines | Data mining | Neural networks | Machine learning | Stations | Algorithms | Computer simulation | Estimating | Multilayer perceptrons | Tools
Multilayer perceptron | Artificial neural network | Wind speed | Forecasting | SPATIAL ESTIMATION | MECHANICS | THERMODYNAMICS | ENERGY & FUELS | FORECASTING WIND | ARTIFICIAL NEURAL-NETWORKS | PREDICTION | Air-turbines | Data mining | Neural networks | Machine learning | Stations | Algorithms | Computer simulation | Estimating | Multilayer perceptrons | Tools
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Loading RightsNeural Processing Letters, ISSN 1370-4621, 2/2017, Volume 45, Issue 1, pp. 29 - 58
This paper introduces the design of the hyperconic multilayer perceptron (HC-MLP). Complex non-linear decision regions for classification purposes are...
Image segmentation | Computational Intelligence | Complex Systems | Computer Science | Artificial neural networks | Artificial Intelligence (incl. Robotics) | Induction motor fault diagnostic | Non-linear decision regions | CLASSIFIER | DESIGN | ALGORITHM | CURRENT SIGNATURE ANALYSIS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ARTIFICIAL NEURAL-NETWORKS | BACKPROPAGATION | PARTICLE SWARM OPTIMIZATION | EVOLUTIONARY SYSTEM | SEGMENTATION | NEURONS | Computer science | Induction electric motors | Algorithms | Image processing | Neural networks | Analysis | Mathematical optimization
Image segmentation | Computational Intelligence | Complex Systems | Computer Science | Artificial neural networks | Artificial Intelligence (incl. Robotics) | Induction motor fault diagnostic | Non-linear decision regions | CLASSIFIER | DESIGN | ALGORITHM | CURRENT SIGNATURE ANALYSIS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ARTIFICIAL NEURAL-NETWORKS | BACKPROPAGATION | PARTICLE SWARM OPTIMIZATION | EVOLUTIONARY SYSTEM | SEGMENTATION | NEURONS | Computer science | Induction electric motors | Algorithms | Image processing | Neural networks | Analysis | Mathematical optimization
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Loading RightsCatena, ISSN 0341-8162, 02/2017, Volume 149, pp. 52 - 63
The main objective of this study is to evaluate and compare the performance of landslide models using machine learning ensemble technique for landslide...
Ensemble techniques | Landslides | Multilayer Perceptron Neural Network | Himalaya | India | 3 GORGES AREA | LOGISTIC-REGRESSION | WATER RESOURCES | SPATIAL PREDICTION | RANDOM SUBSPACE METHOD | PREDICTION CAPABILITY | SOIL SCIENCE | GEOSCIENCES, MULTIDISCIPLINARY | EVIDENTIAL BELIEF FUNCTIONS | SUPPORT VECTOR MACHINE | CLASSIFIER ENSEMBLE | DECISION-TREE MODEL | CONDITIONAL-PROBABILITY | Neural networks | Analysis | Machine learning | Geographic information systems | Computer science | Usage
Ensemble techniques | Landslides | Multilayer Perceptron Neural Network | Himalaya | India | 3 GORGES AREA | LOGISTIC-REGRESSION | WATER RESOURCES | SPATIAL PREDICTION | RANDOM SUBSPACE METHOD | PREDICTION CAPABILITY | SOIL SCIENCE | GEOSCIENCES, MULTIDISCIPLINARY | EVIDENTIAL BELIEF FUNCTIONS | SUPPORT VECTOR MACHINE | CLASSIFIER ENSEMBLE | DECISION-TREE MODEL | CONDITIONAL-PROBABILITY | Neural networks | Analysis | Machine learning | Geographic information systems | Computer science | Usage
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Loading RightsMedical Physics, ISSN 0094-2405, 12/2017, Volume 44, Issue 12, pp. 6209 - 6224
Purpose To reconstruct MR images from subsampled data, we propose a fast reconstruction method using the multilayer perceptron (MLP) algorithm. Methods and...
magnetic resonance imaging (MRI) | multilayer perceptron (MLP) | parallel imaging | artificial neural networks (ANN) | machine learning | TRANSMISSION MEASUREMENTS | SINGULAR-VALUE DECOMPOSITION | SPECTRUM ESTIMATION | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
magnetic resonance imaging (MRI) | multilayer perceptron (MLP) | parallel imaging | artificial neural networks (ANN) | machine learning | TRANSMISSION MEASUREMENTS | SINGULAR-VALUE DECOMPOSITION | SPECTRUM ESTIMATION | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
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Loading RightsExpert Systems With Applications, ISSN 0957-4174, 2011, Volume 38, Issue 10, pp. 13475 - 13481
â–º We consider a multilayer perceptron neural network model for the diagnosis of epilepsy. â–º EEG signals are decomposed into frequency sub-bands using discrete...
Discrete wavelet transform (DWT) | EEG signals | Multilayer perceptron neural network (MLPNN) | Epilepsy | K-means clustering | Classification | ALERTNESS LEVEL | LOGISTIC-REGRESSION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | PREDICTION | ENGINEERING, ELECTRICAL & ELECTRONIC | AUTOMATIC RECOGNITION | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | EEC signals | EPILEPTIC SEIZURES | COEFFICIENTS | WAVELET TRANSFORM | Usage | Electroencephalography | Algorithms | Neural networks | Analysis
Discrete wavelet transform (DWT) | EEG signals | Multilayer perceptron neural network (MLPNN) | Epilepsy | K-means clustering | Classification | ALERTNESS LEVEL | LOGISTIC-REGRESSION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | PREDICTION | ENGINEERING, ELECTRICAL & ELECTRONIC | AUTOMATIC RECOGNITION | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | EEC signals | EPILEPTIC SEIZURES | COEFFICIENTS | WAVELET TRANSFORM | Usage | Electroencephalography | Algorithms | Neural networks | Analysis
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Loading RightsMEDICAL PHYSICS, ISSN 0094-2405, 12/2017, Volume 44, Issue 12, pp. 6209 - 6224
Purpose: To reconstruct MR images from subsampled data, we propose a fast reconstruction method using the multilayer perceptron (MLP) algorithm. Methods and...
magnetic resonance imaging (MRI) | multilayer perceptron (MLP) | parallel imaging | DEEP NEURAL-NETWORKS | artificial neural networks (ANN) | RECONSTRUCTION | machine learning | SENSE | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
magnetic resonance imaging (MRI) | multilayer perceptron (MLP) | parallel imaging | DEEP NEURAL-NETWORKS | artificial neural networks (ANN) | RECONSTRUCTION | machine learning | SENSE | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
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Loading RightsNature Communications, ISSN 2041-1723, 12/2018, Volume 9, Issue 1, pp. 2331 - 7
The progress in the field of neural computation hinges on the use of hardware more efficient than the conventional microprocessors. Recent works have shown...
NEURAL-NETWORKS | ANALOG | RECOGNITION | MEMORY | SYNAPSES | MULTIDISCIPLINARY SCIENCES | DEVICE | CLASSIFICATION | Metal oxides | Fabrication | Multilayers | Classifiers | Microprocessors | Computer simulation | Functional morphology | Technology | Circuits | Memristors | Multilayer perceptrons | Hardware
NEURAL-NETWORKS | ANALOG | RECOGNITION | MEMORY | SYNAPSES | MULTIDISCIPLINARY SCIENCES | DEVICE | CLASSIFICATION | Metal oxides | Fabrication | Multilayers | Classifiers | Microprocessors | Computer simulation | Functional morphology | Technology | Circuits | Memristors | Multilayer perceptrons | Hardware
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Loading RightsIEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, 11/2016, Volume 27, Issue 11, pp. 2146 - 2159
In this paper, we consider the learning problem of multilayer perceptrons (MLPs) formulated as the problem of minimizing a smooth error function. As well...
Training | feedforward neural networks | optimization methods for supervised learning | Minimization | Linear programming | Approximation algorithms | Decomposition techniques | multilayer perceptrons (MLPs) | Indexes | extreme learning machine (ELM) | Optimization | Convergence | Extreme learning machine (ELM) | Optimization methods for supervised learning | Feedforward neural networks | Multilayer perceptrons (MLPs) | COMPUTER SCIENCE, HARDWARE & ARCHITECTURE | HIDDEN NODES | NETWORKS | EXTREME LEARNING-MACHINE | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | BORWEIN GRADIENT-METHOD | ENGINEERING, ELECTRICAL & ELECTRONIC | MINIMIZATION | BARZILAI | CONVERGENCE | OPTIMIZATION | COMPUTER SCIENCE, THEORY & METHODS
Training | feedforward neural networks | optimization methods for supervised learning | Minimization | Linear programming | Approximation algorithms | Decomposition techniques | multilayer perceptrons (MLPs) | Indexes | extreme learning machine (ELM) | Optimization | Convergence | Extreme learning machine (ELM) | Optimization methods for supervised learning | Feedforward neural networks | Multilayer perceptrons (MLPs) | COMPUTER SCIENCE, HARDWARE & ARCHITECTURE | HIDDEN NODES | NETWORKS | EXTREME LEARNING-MACHINE | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | BORWEIN GRADIENT-METHOD | ENGINEERING, ELECTRICAL & ELECTRONIC | MINIMIZATION | BARZILAI | CONVERGENCE | OPTIMIZATION | COMPUTER SCIENCE, THEORY & METHODS
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Loading RightsIEEE Transactions on Audio, Speech, and Language Processing, ISSN 1558-7916, 01/2012, Volume 20, Issue 1, pp. 23 - 29
This paper introduces the sparse multilayer perceptron (SMLP) which jointly learns a sparse feature representation and nonlinear classifier boundaries to...
Training | Multilayer perceptron (MLP) | Neurons | phoneme recognition | Hidden Markov models | Speech recognition | sparse features | Multilayer perceptrons | Cost function | Acoustics | FEATURES | ENGINEERING, ELECTRICAL & ELECTRONIC
Training | Multilayer perceptron (MLP) | Neurons | phoneme recognition | Hidden Markov models | Speech recognition | sparse features | Multilayer perceptrons | Cost function | Acoustics | FEATURES | ENGINEERING, ELECTRICAL & ELECTRONIC
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Loading RightsApplied Soft Computing Journal, ISSN 1568-4946, 04/2015, Volume 29, pp. 65 - 74
The knowledge discovery process is supported by data files information gathered from collected data sets, which often contain errors in the form of missing...
Multilayer perceptron | Hot-deck model | Mean/mode model | Multiple imputation | Regression model | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | MISSING VALUES | TREES | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Multilayer perceptron | Hot-deck model | Mean/mode model | Multiple imputation | Regression model | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | MISSING VALUES | TREES | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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Loading RightsApplied Soft Computing Journal, ISSN 1568-4946, 03/2015, Volume 28, pp. 394 - 399
Dialkylimidazolium-based ionic liquids (ILs) are one of the most employed and accessible ILs. These novel chemicals possess unique physicochemical properties...
Multilayer perceptron | Molecular weights | Dialkylimidazolium-based ionic liquids | Refractive index | Water content | MIXTURES | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | WATER-CONTENT | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ARTIFICIAL NEURAL-NETWORKS | CHLORIDE | Equipment and supplies | Fiber optics
Multilayer perceptron | Molecular weights | Dialkylimidazolium-based ionic liquids | Refractive index | Water content | MIXTURES | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | WATER-CONTENT | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ARTIFICIAL NEURAL-NETWORKS | CHLORIDE | Equipment and supplies | Fiber optics
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Loading RightsJournal of Electronic Imaging, ISSN 1017-9909, 7/2018, Volume 27, Issue 4, pp. 043022 - 043022
This paper presents an algorithm to improve images with hazing effects. Usually, the dehazing methods based on the dark channel prior make use of two different...
multilayer perceptron | artificial neural network | image enhancement | defogging | single image dehazing | dark channel prior | REMOVAL | IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY | OPTICS | POLARIZATION | ENGINEERING, ELECTRICAL & ELECTRONIC
multilayer perceptron | artificial neural network | image enhancement | defogging | single image dehazing | dark channel prior | REMOVAL | IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY | OPTICS | POLARIZATION | ENGINEERING, ELECTRICAL & ELECTRONIC
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Loading RightsTransactions in GIS, ISSN 1361-1682, 10/2019, Volume 23, Issue 5, pp. 1048 - 1077
In this article, multilayer perceptron (MLP) network models with spatial constraints are proposed for regionalization of geostatistical point data based on...
Geological surveys | Data mining | Geospatial data | Analysis | Machine learning | Spatial discrimination learning | Geostatistics | Multivariate analysis | Learning algorithms | Algorithms | Spatial data | Data sets | Classification | Multilayer perceptrons | Homogeneity | Constraint modelling | Autocorrelation | Spatial memory
Geological surveys | Data mining | Geospatial data | Analysis | Machine learning | Spatial discrimination learning | Geostatistics | Multivariate analysis | Learning algorithms | Algorithms | Spatial data | Data sets | Classification | Multilayer perceptrons | Homogeneity | Constraint modelling | Autocorrelation | Spatial memory
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Loading RightsTheoretical and Applied Climatology, ISSN 0177-798X, 8/2018, Volume 133, Issue 3, pp. 1119 - 1131
An accurate computational approach for the prediction of pan evaporation over daily time horizons is a useful decisive tool in sustainable agriculture and...
Firefly Algorithm | Climatology | Earth Sciences | Pan evaporation | Multilayer perceptron | Atmospheric Sciences | Hybrid model | Waste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution | Support vector machine | Forecasting | Atmospheric Protection/Air Quality Control/Air Pollution | REGRESSION | SUPPORT VECTOR MACHINES | PERFORMANCE | ARTIFICIAL NEURAL-NETWORKS | ABSOLUTE ERROR MAE | COMPUTING TECHNIQUE | TEMPERATURE | REFERENCE EVAPOTRANSPIRATION | GROUNDWATER LEVELS | INDEX | METEOROLOGY & ATMOSPHERIC SCIENCES | Weather | Case studies | Water | Technology application | Algorithms | Analysis | Water use | Management | Root-mean-square errors | Irrigation | Evaporation data | Allocations | Model testing | Irrigation systems | Heuristic methods | Performance prediction | Multilayers | Sustainable agriculture | Mathematical models | Agriculture | Hydrologic models | Water resources | Agricultural management | Meteorological stations | Evaporation | Railway stations | Support vector machines | Hydrology | Utilization | Computer applications | Multilayer perceptrons | Performance enhancement | Resource management
Firefly Algorithm | Climatology | Earth Sciences | Pan evaporation | Multilayer perceptron | Atmospheric Sciences | Hybrid model | Waste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution | Support vector machine | Forecasting | Atmospheric Protection/Air Quality Control/Air Pollution | REGRESSION | SUPPORT VECTOR MACHINES | PERFORMANCE | ARTIFICIAL NEURAL-NETWORKS | ABSOLUTE ERROR MAE | COMPUTING TECHNIQUE | TEMPERATURE | REFERENCE EVAPOTRANSPIRATION | GROUNDWATER LEVELS | INDEX | METEOROLOGY & ATMOSPHERIC SCIENCES | Weather | Case studies | Water | Technology application | Algorithms | Analysis | Water use | Management | Root-mean-square errors | Irrigation | Evaporation data | Allocations | Model testing | Irrigation systems | Heuristic methods | Performance prediction | Multilayers | Sustainable agriculture | Mathematical models | Agriculture | Hydrologic models | Water resources | Agricultural management | Meteorological stations | Evaporation | Railway stations | Support vector machines | Hydrology | Utilization | Computer applications | Multilayer perceptrons | Performance enhancement | Resource management
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An efficient hybrid multilayer perceptron neural network with grasshopper optimization
Soft Computing, ISSN 1432-7643, 9/2019, Volume 23, Issue 17, pp. 7941 - 7958
This paper proposes a new hybrid stochastic training algorithm using the recently proposed grasshopper optimization algorithm (GOA) for multilayer perceptrons...
Engineering | Grasshopper Optimization Algorithm | Computational Intelligence | Control, Robotics, Mechatronics | Artificial Intelligence | Classification | Multilayer perceptron | Medical diagnosis | Mathematical Logic and Foundations | Optimization | DESIGN | ANT COLONY OPTIMIZATION | PSO | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | GENETIC ALGORITHM | BACKPROPAGATION | PARTICLE SWARM OPTIMIZATION | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | DIFFERENTIAL EVOLUTION ALGORITHM | SUPPORT VECTOR MACHINE | SELECTION | Algorithms | Neural networks | Coronary heart disease | Analysis
Engineering | Grasshopper Optimization Algorithm | Computational Intelligence | Control, Robotics, Mechatronics | Artificial Intelligence | Classification | Multilayer perceptron | Medical diagnosis | Mathematical Logic and Foundations | Optimization | DESIGN | ANT COLONY OPTIMIZATION | PSO | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | GENETIC ALGORITHM | BACKPROPAGATION | PARTICLE SWARM OPTIMIZATION | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | DIFFERENTIAL EVOLUTION ALGORITHM | SUPPORT VECTOR MACHINE | SELECTION | Algorithms | Neural networks | Coronary heart disease | Analysis
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