Comparison of neural classifiers for vehicles gear estimation

Wefky, Ahmed; Espinosa, Felipe; Prieto, Ángel Weruaga; Garcı́a, J.J.; Barrios, Corrales

doi:10.1016/j.asoc.2011.01.030

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…In analyzing results from the pattern recognition techniques, we note that although LVQ-ANN did not produce very accurate results, when comparing this approach with other ANNs, LVQ has the advantage that it can classify any set of input vectors, has a fast learning algorithm [61] and is used extensively in the literatures [62] , [63] , [64] , [65] , [66] , [67] , [68] .…”

Section: Discussionmentioning

confidence: 99%

Classification of Camellia (Theaceae) Species Using Leaf Architecture Variations and Pattern Recognition Techniques

Lü

Ghiassi

et al. 2012

PLoS ONE

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Leaf characters have been successfully utilized to classify Camellia (Theaceae) species; however, leaf characters combined with supervised pattern recognition techniques have not been previously explored. We present results of using leaf morphological and venation characters of 93 species from five sections of genus Camellia to assess the effectiveness of several supervised pattern recognition techniques for classifications and compare their accuracy. Clustering approach, Learning Vector Quantization neural network (LVQ-ANN), Dynamic Architecture for Artificial Neural Networks (DAN2), and C-support vector machines (SVM) are used to discriminate 93 species from five sections of genus Camellia (11 in sect. Furfuracea, 16 in sect. Paracamellia, 12 in sect. Tuberculata, 34 in sect. Camellia, and 20 in sect. Theopsis). DAN2 and SVM show excellent classification results for genus Camellia with DAN2's accuracy of 97.92% and 91.11% for training and testing data sets respectively. The RBF-SVM results of 97.92% and 97.78% for training and testing offer the best classification accuracy. A hierarchical dendrogram based on leaf architecture data has confirmed the morphological classification of the five sections as previously proposed. The overall results suggest that leaf architecture-based data analysis using supervised pattern recognition techniques, especially DAN2 and SVM discrimination methods, is excellent for identification of Camellia species.

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Section: Discussionmentioning

confidence: 99%

Classification of Camellia (Theaceae) Species Using Leaf Architecture Variations and Pattern Recognition Techniques

Lü

Ghiassi

et al. 2012

PLoS ONE

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“…Neural models have been developed to estimate the pedals activity in terms of the engine RPM, vehicle velocity, linear acceleration, and the frontal inclination in [20]. Moreover, a neural classifier has been proposed to determine the gear position as a function of the vehicle speed and the engine RPM in [21]. Some studies have used the vehicle velocity and acceleration to describe the operational modes of the vehicle [7,8,10,22].…”

Section: Introductionmentioning

confidence: 99%

On-Road Magnetic Emissions Prediction of Electric Cars in Terms of Driving Dynamics Using Neural Networks

Wefky¹,

Espinosa²,

Leferink³

et al. 2013

PIER

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Abstract-This paper presents a novel artificial neural network (ANN) model estimating vehicle-level radiated magnetic emissions of an electric car as a function of the corresponding driving pattern. Real world electromagnetic interference (EMI) experiments have been realized in a semi-anechoic chamber using Renault Twizy. Time-domain electromagnetic interference (TDEMI) measurement techniques have been employed to record the radiated disturbances in the 150 kHz-30 MHz range. Interesting emissions have been found in the range 150 kHz-3.8 MHz approximately. The instantaneous vehicle speed and acceleration have been chosen to represent the vehicle operational modes. A comparative study of the prediction performance between different static and dynamic neural networks has been done. Results showed that a Multilayer Perceptron (MLP) model trained with extreme learning machines (ELM) has achieved the best prediction results. The proposed model has been used to estimate the radiated magnetic field levels of an urban trip carried out with a Think City electric car.

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Hybridization of multi-objective evolutionary algorithms and artificial neural networks for optimizing the performance of electrical drives

Zvoianu

Bramerdorfer

Lughofer

et al. 2013

Engineering Applications of Artificial Intelligence

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Comparison of neural classifiers for vehicles gear estimation

Cited by 5 publications

References 23 publications

Classification of Camellia (Theaceae) Species Using Leaf Architecture Variations and Pattern Recognition Techniques

Classification of Camellia (Theaceae) Species Using Leaf Architecture Variations and Pattern Recognition Techniques

On-Road Magnetic Emissions Prediction of Electric Cars in Terms of Driving Dynamics Using Neural Networks

Hybridization of multi-objective evolutionary algorithms and artificial neural networks for optimizing the performance of electrical drives

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