Artificial Neural Network Architectures and Training Processes

Silva, Ivan Nunes da; Spatti, Danilo Hernane; Flauzino, Rogério Andrade; Liboni, Luisa Helena Bartocci; Alves, Silas Franco dos Reis

doi:10.1007/978-3-319-43162-8_2

Cited by 113 publications

(70 citation statements)

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“…Neural networks can have single or multiple layers, with nodes or neurons interconnected that allows signals to travel through the network. ANNs are typically divided into three layers of neurons, namely: input (receives the information), hidden (extracts patterns and performs the internal processing), and output (presents the final network output) [32,33]. Training is the process to optimize parameters [34].…”

Section: Discussionmentioning

confidence: 99%

Dental Images Recognition Technology and Applications: A Literature Review

Prados-Privado

Villalón²,

Martínez-Martínez³

et al. 2020

Applied Sciences

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Neural networks are increasingly being used in the field of dentistry. The aim of this literature review was to visualize the state of the art of artificial intelligence in dental applications, such as the detection of teeth, caries, filled teeth, crown, prosthesis, dental implants and endodontic treatment. A search was conducted in PubMed, the Institute of Electrical and Electronics Engineers (IEEE) Xplore and arXiv.org. Data extraction was performed independently by two reviewers. Eighteen studies were included. The variable teeth was the most analyzed (n = 9), followed by caries (n = 7). No studies detecting dental implants and filled teeth were found. Only two studies investigated endodontic applications. Panoramic radiographies were the most common image employed (n = 5), followed by periapical images (n = 3). Near-infrared light transillumination images were employed in two studies and bitewing and computed tomography (CT) were employed in one study. The included articles used a wide variety of neuronal networks to detect the described variables. In addition, the database used also had a great heterogeneity in the number of images. A standardized methodology should be used in order to increase the compatibility and robustness between studies because of the heterogeneity in the image database, type, neural architecture and results.

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Section: Discussionmentioning

confidence: 99%

Dental Images Recognition Technology and Applications: A Literature Review

Prados-Privado

Villalón²,

Martínez-Martínez³

et al. 2020

Applied Sciences

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“…By depending on the disposition of neurons and the composition of the layers, the architectures of the ANN is classified as recurrent NN, single-layer feed-forward NN, and multilayer feed-forward NN [ 48 ]. Multilayer Perceptron (MLP), whose general structure is shown in Figure 3 , uses multilayer feed-forward architecture and is the most commonly applied network.…”

Section: Methodsmentioning

confidence: 99%

Prediction of Attendance Demand in European Football Games: Comparison of ANFIS, Fuzzy Logic, and ANN

Şahin

Erol

2018

Computational Intelligence and Neuroscience

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An artificial neural network (ANN), adaptive neurofuzzy inference system (ANFIS) models, and fuzzy rule-based system (FRBS) models are developed to predict the attendance demand in European football games, in this paper. To determine the most successful method, each of the methods is analyzed under different situations. The Elman backpropagation, feed-forward backpropagation, and cascade-forward backpropagation network types are developed to determine the outperforming ANN model. The backpropagation and hybrid optimization methods are used for training fuzzy inference system (FIS) to determine the outperforming ANFIS model. The fuzzy logic model is developed after experimenting different forms of membership functions. To this end, the data of 236 soccer games are used to train the ANN and ANFIS models, and 2017/2018 season's data of these clubs are used to test all of the models. The results of all models are compared with each other and real past data. To assess the performance of each model, two error measures that are Mean Absolute Percent Error (MAPE) and Mean Absolute Deviation (MAD) are implemented. These measures reveal that the ANN model that has Elman network type outperforms the other models. Finally, the results emphasize that the proposed ANN model can be effectively used for prediction purposes.

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“…This technique is known as a method of least squares [15]. Therefore, the training process of a neural network involves tune of the value of the weights and the biases of the networks to optimize network performance, as defined by the network performance function [16].…”

Section: Failure Analysis In a Power System And Assessment Methodsmentioning

confidence: 99%

Condition assessment of power transformers status based on moisture level using fuzzy logic techniques

Rexhepi

Nakov

2018

J. Mechatron. Electr. Power Veh. Technol.

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Power transformers are one of the most expensive components; therefore the focus on their status and its continuous operation is the primary task. In the power systems, condition assessment of performance and reliability is based on the state of components, measurements, testing, and maintenance as well as their diagnosis. Hence, condition assessment of power transformer parameters is important regarding their status and finding incipient failures. Among many factors, the most factors that affect the safe operation and life expectancy of the transformer is the moisture in oil. It is known that the low moisture oil in power transformers causes many problems including electrical breakdown, increase the amount of partial discharge, decreases the dielectric withstand strength and other phenomena. Thus, knowledge about the moisture concentration in a power transformer is significantly important for safe operation and lifespan. In this study, moisture level in oil is estimated, and its status classification is proposed by using fuzzy logic techniques for the power transformer monitoring and condition assessment. Moreover, the goal of the study is to find methods and techniques for the condition assessment of power transformers status based on the state of moisture in oil using the fuzzy logic technique. These applied techniques increase the power system reliability, help to reduce incipient failures and give the better maintenance plan using an algorithm based on logic rules. Also, by using the fuzzy logic techniques, it is easier to prevent failures which may have consequences not only for transformers but also for the power system as a whole.

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Artificial Neural Network Architectures and Training Processes

Cited by 113 publications

References 0 publications

Dental Images Recognition Technology and Applications: A Literature Review

Dental Images Recognition Technology and Applications: A Literature Review

Prediction of Attendance Demand in European Football Games: Comparison of ANFIS, Fuzzy Logic, and ANN

Condition assessment of power transformers status based on moisture level using fuzzy logic techniques

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