Perceptron neural networks
System identification of hammerstein model a quarter car passive suspension systems using Multilayer Perceptron Neural Networks (MPNN)
... Abstract. Recently, some researchers have focused on the applications of neural networks for system identification. In this paper, a Hammerstein model of a quarter car passive suspension system is ...
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Characterization of Lossy SIW Resonators Based on Multilayer Perceptron Neural Networks on Graphics Processing Unit
... Multilayer Perceptron Neural Networks (MLPNN) on GPU, has been ...Artificial Neural Networks (ANNs) have been recognized as useful alternative to conventional approaches usually ...
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A Survey on Applications of Multi Layer Perceptron Neural Networks in DOA Estimation for Smart Antennas
... wireless networks and therefore, it is one of the most significant areas of study in wireless communications ...establishing networks optimize quality of service and support operational transparency in the ...
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Reducing Error Signal in Multilayer Perceptron Neural Networks using MLP for Label Ranking
... A Back Propagation network learns by example. You give the algorithm examples of what you want the network to do and it changes the network’s weights so that, when training is finished, it will give you the required ...
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Comparative Application of Radial Basis Function and Multilayer Perceptron Neural Networks to Predict Traffic Noise Pollution in Tehran Roads
... as neural network has led to novel solutions for this ...of neural networks – multilayer perceptron and radial basis function – were developed for predicting equivalent continuous sound level ...
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Evaluating the effect of salinity on corn grain yield using multilayer perceptron neural networks
... MS where the degree of dissimilarity coefficient between MCW and MLR was 1.1563, Connection Weights (CW), Partial Derivatives (PD), (Gervey et al., 2003), and Profile Method (PM) showed moderate performance ...
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Efficiency of Multilayer Perceptron Neural Networks Powered by Multi Verse Optimizer
... In this work, multi-layer feedforward perceptron network was trained by a promising metaheuristic approach, MVO. The framework is examined by five datasets and two trigonometric functions. The framework is tested ...
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THE TRANSITION FROM 4G TO 5G BY EMPLOYING FEMTO CELLS PROVEN THROUGH DATA RATE, PLR AND DELAY
... multilayer perceptron neural networks, support vector machines, decision trees, random forest, adaptive boosting of trees and k – nearest neighbour used to automatically detect and filter hosts that ...
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Combining gene expression programming and genetic algorithm as a powerful hybrid modeling approach for pear rootstocks tissue culture media formulation
... multi-layer perceptron neural networks (MLPNN) and Multiple Linear Regression (MLR) ...Function Neural Network (RBFNN) and Gene Expression Programming ...
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Algorithm and software based on MLPNN for estimating channel use in the spectral decision stage in cognitive radio networks
... multilayer perceptron neural networks (MLPNN) technique is proposed to estimate the future state of use of a licensed channel by primary users (PUs); this will be useful at the spectral decision ...
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Estimation of soil parameters over bare agriculture areas from C band polarimetric SAR data using neural networks
... Inversion approaches using a priori information on soil pa- rameters were developed to improve soil moisture retrieval from SAR data. Satalino et al. (2002) developed an algo- rithm to retrieve soil moisture content over ...
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Application of Artificial Neural Network and Adaptive Neural based Fuzzy Inference System Techniques in Estimating of Virtual Water
... Multilayer Perceptron (MLP), Radial Basis Functions (RBF), and Generalized Regression Neural Networks (GRNN) as well as ANFIS were examined in estimation of VW using measured data of the yield and ...
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Detection of mastitis and its stage of progression by automatic milking systems using artificial neural networks
... artificial neural networks, multilayer perceptron (MLP) and self-organizing feature map (SOM) were used to detect mastitis by automatic milking systems (AMS) using a new mastitis indicator that ...
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Data Aggregation Framework for Clustered Sensor Networks Using Multi Layer Perceptron Neural Network
... The achievability of the proposed scheme is evaluated through performance analysis and simulation results. The results show that proposed scheme outperforms well compared to the existing schemes in terms of minimizes the ...
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Deep learning for land cover and land use classification
... 41 overlapped patches introduces too much redundant computations, thus, severely restricting the actual utility of the method for large-scale land cover classification (Fu et al. 2017, Maggiori et al. 2017). Recent ...
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A Comparative Study of Nonlinear Time Varying Process Modeling Techniques: Application to Chemical Reactor
... model is optimized by gradient descent method and ge- netic algorithms. Each optimized RBF models are com- pared with multilayer perceptron. Mean square error is carried out to evaluate performance of both models ...
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Open Journal Systems
... There are many existing rainfall prediction models, which employed different models and methodologies such as numerical, statistical, and machine learning models (Abbot & Marohasy, 2012; Adamowski & Sun, 2010; ...
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EMBEDDING RАPIDMINER MODELS IN JAVA CODE
... Artificial Neural Networks is an important application of Artificial Intelligence and is an attempt to model the functioning of the human ...outputs. Neural Networks provide a mapping between ...
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Neural Network Design - Free Computer, Programming, Mathematics, Technical Books, Lecture Notes and Tutorials
... of neural networks become ap- parent only for large-scale problems, which are computationally intensive and not feasible for hand ...MATLAB, neural network al- gorithms can be quickly implemented, ...
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Gait Recognition Using Deep Learning
... ABSTRACT: Detection of moving gait in video streams is the first relevant step of information extraction in many computer vision applications. Aside from the intrinsic usefulness of being able to segment video streams ...
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