Log in

Relevant bibliographies by topics / ANN-training algorithms / Journal articles

To see the other types of publications on this topic, follow the link: ANN-training algorithms.

Journal articles on the topic 'ANN-training algorithms'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'ANN-training algorithms.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Prasad, Bhawesh, Raj Kumar, and Manmohan Singh. "Performance analysis of various training algorithms of deep learning based controller." Engineering Research Express 5, no. 2 (2023): 025038. http://dx.doi.org/10.1088/2631-8695/acd3d5.

Full text

Abstract:

Abstract Advances in artificial neural networks (ANN), specifically deep learning (DL), have widened the application domain of process control. DL algorithms and models have become quite common these days. The training algorithm is the most important part of an ANN that affects the performance of the controller. Training algorithms optimize the weights and biases of the ANN according to the input-output patterns. In this paper, the performance of different training algorithms was evaluated, analysed, and compared in a feed-forward backpropagation architecture. The training algorithms were simu

APA, Harvard, Vancouver, ISO, and other styles

2

Noor, Hafidz, Sfenrianto, Pribadi Yogie, Fitri Evita, and Ratino. "ANN and SVM algorithm in Divorce Predictor." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 2523–27. https://doi.org/10.35940/ijeat.C5902.029320.

Full text

Abstract:

Classification is a technique used to predict group membership or label for data samples (instances). In order to predict the result, the classification algorithm processes the training set, which contains a set of attributes and corresponding results. One of these classification technique is implemented in order to predict divorce in Turkey. This research is executed by Yöntem, M. K. et al. in 2019. In this , M. K. concluded that the ANN algorithm combined with correlation-based feature selection has the best performance with an accuracy of 98.82% and Kappa value of 0.9765. Nevertheless,

APA, Harvard, Vancouver, ISO, and other styles

3

ANUSHKA, PERERA, AZAMATHULLA HAZI MD., and RATHNAYAKE UPAKA. "Comparison of different artificial neural network (ANN) training algorithms to predict the atmospheric temperature in Tabuk, Saudi Arabia." MAUSAM 71, no. 2 (2021): 233–44. http://dx.doi.org/10.54302/mausam.v71i2.22.

Full text

Abstract:

Use of Artificial neural network (ANN) models to predict weather parameters has become important over the years. ANN models give more accurate results in weather and climate forecasting among many other methods. However, different models require different data and these data have to be handled accordingly, but carefully. In addition, most of these data are from non-linear processes and therefore, the prediction models are usually complex. Nevertheless, neural networks perform well for non-linear data and produce well acceptable results. Therefore, this study was carried out to compare differen

APA, Harvard, Vancouver, ISO, and other styles

4

Karim, Hesam, Sharareh R. Niakan, and Reza Safdari. "Comparison of Neural Network Training Algorithms for Classification of Heart Diseases." IAES International Journal of Artificial Intelligence (IJ-AI) 7, no. 4 (2018): 185. http://dx.doi.org/10.11591/ijai.v7.i4.pp185-189.

Full text

Abstract:

<span lang="EN-US">Heart disease is the first cause of death in different countries. Artificial neural network (ANN) technique can be used to predict or classification patients getting a heart disease. There are different training algorithms for ANN. We compared eight neural network training algorithms for classification of heart disease data from UCI repository containing 303 samples. Performance measures of each algorithm containing the speed of training, the number of epochs, accuracy, and mean square error (MSE) were obtained and analyzed. Our results showed that training time for gr

APA, Harvard, Vancouver, ISO, and other styles

5

Hesam, Karim, R. Niakan Sharareh, and Safdari Reza. "Comparison of Neural Network Training Algorithms for Classification of Heart Diseases." International Journal of Artificial Intelligence (IJ-AI) 7, no. 4 (2018): 185–89. https://doi.org/10.11591/ijai.v7.i4.pp185-189.

Full text

Abstract:

Heart disease is the first cause of death in different countries. Artificial neural network (ANN) technique can be used to predict or classification patients getting a heart disease. There are different training algorithms for ANN. We compared eight neural network training algorithms for classification of heart disease data from UCI repository containing 303 samples. Performance measures of each algorithm containing the speed of training, the number of epochs, accuracy, and mean square error (MSE) were obtained and analyzed. Our results showed that training time for gradient descent algorithms

APA, Harvard, Vancouver, ISO, and other styles

6

Al-Momani, Mohammad, Seba Al-Gharaibeh, Ali Al-Dmour, and Allaham Ahmed. "Islanding Detection Method Based Artificial Neural Network." Jordan Journal of Energy 1, no. 1 (2022): 19–36. http://dx.doi.org/10.35682/jje.v1i1.3.

Full text

Abstract:

This paper presents a new islanding detection technique based on an artificial neural network (ANN) for a doubly fed induction wind turbine (DFIG). This technique takes advantage of ANN as pattern classifiers. Five different ANN systems are presented in this paper based on various inputs: three phase power, phase voltage, phase current, neutral voltage, and neutral current. An ANN structure is trained for each input, and the comparison between the different structures is presented. Feedforward ANN structures are used for the five systems. Three different learning algorithms are used: backpropa

APA, Harvard, Vancouver, ISO, and other styles

7

Baştemur Kaya, Ceren Baştemur. "On Performance of Marine Predators Algorithm in Training of Feed-Forward Neural Network for Identification of Nonlinear Systems." Symmetry 15, no. 8 (2023): 1610. http://dx.doi.org/10.3390/sym15081610.

Full text

Abstract:

Artificial neural networks (ANNs) are used to solve many problems, such as modeling, identification, prediction, and classification. The success of ANN is directly related to the training process. Meta-heuristic algorithms are used extensively for ANN training. Within the scope of this study, a feed-forward artificial neural network (FFNN) is trained using the marine predators algorithm (MPA), one of the current meta-heuristic algorithms. Namely, this study is aimed to evaluate the performance of MPA in ANN training in detail. Identification/modeling of nonlinear systems is chosen as the probl

APA, Harvard, Vancouver, ISO, and other styles

8

Lin, Jyh-Woei. "Is the Algorithm of Artificial Neural Network a Deduction or Induction? Discussion between Natural Sciences, Mathematics and Philosophy." European Journal of Information Technologies and Computer Science 1, no. 4 (2021): 6–8. http://dx.doi.org/10.24018/compute.2021.1.4.29.

Full text

Abstract:

The algorithm of artificial neural network (ANN) has been defined as a supervised learning and heuristic algorithms. In training an ANN model, big data is necessary to use as training data to obtain perfectly accurate predicted data. However, big data really have no clear definition. Therefore, adding new training data to re-train an ANN model, by which can improve the predicted accuracy. This action of re-training this ANN model with added new training data is repeated to approach the laws of physics that is accessed to the principle of induction e.g., empirical formulas. However, accessing t

APA, Harvard, Vancouver, ISO, and other styles

9

Nazmi, Nurhazimah, Mohd Azizi Abdul Rahman, Saiful Amri Mazlan, et al. "Analysis of EMG Signals during Stance and Swing Phases for Controlling Magnetorheological Brake applications." Open Engineering 11, no. 1 (2020): 112–19. http://dx.doi.org/10.1515/eng-2021-0009.

Full text

Abstract:

AbstractThe development of ankle foot orthoses (AFO) for lower limb rehabilitation have received significant attention over the past decades. Recently, passive AFO equipped with magnetorheological brake had been developed based on ankle angle and electromyography (EMG) signals. Nonetheless, the EMG signals were categorized in stance and swing phases through visual observation as the signals are stochastic. Therefore, this study aims to classify the pattern of EMG signals during stance and swing phases. Seven-time domains features will be extracted and fed into artificial neural network (ANN) a

APA, Harvard, Vancouver, ISO, and other styles

10

Manjula Devi, R., S. Kuppuswami, and R. C. Suganthe. "Fast Linear Adaptive Skipping Training Algorithm for Training Artificial Neural Network." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/346949.

Full text

Abstract:

Artificial neural network has been extensively consumed training model for solving pattern recognition tasks. However, training a very huge training data set using complex neural network necessitates excessively high training time. In this correspondence, a new fast Linear Adaptive Skipping Training (LAST) algorithm for training artificial neural network (ANN) is instituted. The core essence of this paper is to ameliorate the training speed of ANN by exhibiting only the input samples that do not categorize perfectly in the previous epoch which dynamically reducing the number of input samples e

APA, Harvard, Vancouver, ISO, and other styles

11

Kamp, R. G., and H. H. G. Savenije. "Optimising training data for ANNs with Genetic Algorithms." Hydrology and Earth System Sciences 10, no. 4 (2006): 603–8. http://dx.doi.org/10.5194/hess-10-603-2006.

Full text

Abstract:

Abstract. Artificial Neural Networks (ANNs) have proved to be good modelling tools in hydrology for rainfall-runoff modelling and hydraulic flow modelling. Representative datasets are necessary for the training phase in which the ANN learns the model's input-output relations. Good and representative training data is not always available. In this publication Genetic Algorithms (GA) are used to optimise training datasets. The approach is tested with an existing hydraulic model in The Netherlands. An initial trainnig dataset is used for training the ANN. After optimisation with a GA of the traini

APA, Harvard, Vancouver, ISO, and other styles

12

Liu, Gui Ling, and Feng Gao. "PSO-BP Combined Artificial Neural Network Method Research." Applied Mechanics and Materials 353-356 (August 2013): 3537–40. http://dx.doi.org/10.4028/www.scientific.net/amm.353-356.3537.

Full text

Abstract:

BP artificial neural network(ANN) based on gradient algorithm method is widely applied, but because the error surface of object function is very complex and the choose of initial value effects network training results, convergence rate is slow and local minimum is likely to fall into. Particle swarm optimization(PSO) algorithm has better global searching ability to get rid the puzzles of falling into local minimum. By adequately studying on the two algorithms’ characteristics, a new type of combined ANN training method is put forward, and PSO-BP ann model is successfully built.

APA, Harvard, Vancouver, ISO, and other styles

13

Meena, Gaurav, and Nekram Rawal. "Artificial Neural Network Modeling for Adsorption Efficiency of Cr(VI) Ion from Aqueous Solution Using Waste Tire Activated Carbon." Nature Environment and Pollution Technology 22, no. 3 (2023): 1481–91. http://dx.doi.org/10.46488/nept.2023.v22i03.033.

Full text

Abstract:

In this study, waste tires were used to develop activated carbon for the adsorption of Cr(VI) from aqueous solutions, and an artificial neural network (ANN) model was applied to predict the adsorption efficiency of waste-tire activated carbon (WTAC). SEM and FTIR were used to characterize the developed WTAC. A three-layer ANN with different training algorithms and hidden layers with different numbers of neurons was developed using 79 data sets gathered from batch adsorption experiments with different initial Cr(VI) ion concentrations, contact periods, temperatures, and doses. Conjugate gradien

APA, Harvard, Vancouver, ISO, and other styles

14

Mirzaaghabeik, Hossein, Nuha S. Mashaan, and Sanjay Kumar Shukla. "A Predictive Model for the Shear Capacity of Ultra-High-Performance Concrete Deep Beams Reinforced with Fibers Using a Hybrid ANN-ANFIS Algorithm." Applied Mechanics 6, no. 2 (2025): 27. https://doi.org/10.3390/applmech6020027.

Full text

Abstract:

Ultra-high-performance concrete (UHPC) has attracted considerable attention from both the construction industry and researchers due to its outstanding durability and exceptional mechanical properties, particularly its high compressive strength. Several factors influence the shear capacity of UHPC deep beams, including compressive strength, the shear span-to-depth ratio (λ), fiber content (FC), vertical web reinforcement (ρsv), horizontal web reinforcement (ρsh), and longitudinal web reinforcement (ρs). Considering these factors, this research proposes a novel hybrid algorithm that combines an

APA, Harvard, Vancouver, ISO, and other styles

15

Ayodele, Bamidele, Siti Mustapa, May Alsaffar, and Chin Cheng. "Artificial Intelligence Modelling Approach for the Prediction of CO-Rich Hydrogen Production Rate from Methane Dry Reforming." Catalysts 9, no. 9 (2019): 738. http://dx.doi.org/10.3390/catal9090738.

Full text

Abstract:

This study investigates the applicability of the Leven–Marquardt algorithm, Bayesian regularization, and a scaled conjugate gradient algorithm as training algorithms for an artificial neural network (ANN) predictively modeling the rate of CO and H2 production by methane dry reforming over a Co/Pr2O3 catalyst. The dataset employed for the ANN modeling was obtained using a central composite experimental design. The input parameters consisted of CH4 partial pressure, CO2 partial pressure, and reaction temperature, while the target parameters included the rate of CO and H2 production. A neural net

APA, Harvard, Vancouver, ISO, and other styles

16

Azadeh, A., M. Sheikhalishahi, and M. Hasumi. "A hybrid intelligent algorithm for optimum forecasting of CO2 emission in complex environments: the cases of Brazil, Canada, France, Japan, India, UK and US." World Journal of Engineering 12, no. 3 (2015): 237–46. http://dx.doi.org/10.1260/1708-5284.12.3.237.

Full text

Abstract:

This study presents a hybrid meta-modeling algorithm for optimum carbon dioxide (CO2) emission estimation. It is composed of artificial neural network (ANN), fuzzy linear regression (FLR), and conventional regression (CR). Different FLR models are considered to cover the latest algorithms and viewpoints. ANN with different training algorithms and transfer functions is also applied to data sets. The proposed hybrid algorithms uses analysis of variance (ANOVA), and mean absolute percentage error (MAPE) to select between ANN, FLR or conventional regression for future CO2 emission estimation. The

APA, Harvard, Vancouver, ISO, and other styles

17

Hussein, Maryam Mahmood, Ammar Hussein Mutlag, and Hussain Shareef. "Developed artificial neural network based human face recognition." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 3 (2019): 1279. http://dx.doi.org/10.11591/ijeecs.v16.i3.pp1279-1285.

Full text

Abstract:

<p>Face recognition has become one of the most important challenging problems in personal computer-human interaction, video observation, and biometric. Many algorithms have been developed in the recent years. Theses algorithms are not sufficiently robust to address the complex images. Therefore, this paper proposes soft computing algorithm based face recognition. One of the most promising soft computing algorithms which is back-propagation artificial neural network (BP-ANN) has been proposed. The proposed BP-ANN has been developed to improve the performance of the face recognition. The i

APA, Harvard, Vancouver, ISO, and other styles

18

Amassmir, Samar, Said Tkatek, Otman Abdoun, and Jaafar Abouchabaka. "An intelligent irrigation system based on internet of things (IoT) to minimize water loss." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 1 (2022): 504. http://dx.doi.org/10.11591/ijeecs.v25.i1.pp504-510.

Full text

Abstract:

This paper proposes a comparison of three machine learning algorithms for a better intelligent irrigation system based on internet of things (IoT) for differents products. This work's major contribution is to specify the most accurate algorithm among the three machine learning algorithms (k-nearest neighbors (KNN), support vector machine (SVM), artificial neural network (ANN)). This is achieved by collecting irrigation data of a specific products and split it into training data and test data then compare the accuracy of the three algorithms. To evaluate the performance of our algorithm we buil

APA, Harvard, Vancouver, ISO, and other styles

19

Amassmir, Samar, Said Tkatek, Otman Abdoun, and Jaafar Abouchabaka. "An intelligent irrigation system based on internet of things (IoT) to minimize water loss." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 1 (2022): 504–10. https://doi.org/10.11591/ijeecs.v25.i1.pp504-510.

Full text

Abstract:

This paper proposes a comparison of three machine learning algorithms for a better intelligent irrigation system based on internet of things (IoT) for differents products. This work's major contribution is to specify the most accurate algorithm among the three machine learning algorithms (k-nearest neighbors (KNN), support vector machine (SVM), artificial neural network (ANN)). This is achieved by collecting irrigation data of a specific products and split it into training data and test data then compare the accuracy of the three algorithms. To evaluate the performance of our algorithm we

APA, Harvard, Vancouver, ISO, and other styles

20

Katha, Kishor Kumar, and Suresh Pabboju. "AGCS Technique to Improve the Performance of Neural Networks." Journal of Intelligent Systems 29, no. 1 (2019): 1235–45. http://dx.doi.org/10.1515/jisys-2017-0423.

Full text

Abstract:

Abstract In this paper, a fresh method is offered regarding training of particular neural networks. This technique is a combination of the adaptive genetic (AG) and cuckoo search (CS) algorithms, called the AGCS method. The intention of training a particular artificial neural network (ANN) is to obtain the finest weight load. With this protocol, a particular weight is taken into account as feedback, which is optimized by means of the hybrid AGCS protocol. In the beginning, a collection of weights is initialized and the similar miscalculation is discovered. Finally, during training of an ANN, w

APA, Harvard, Vancouver, ISO, and other styles

21

Kim, Soo-Jin, Seung-Jong Bae, Seung-Jae Lee, and Min-Won Jang. "Monthly Agricultural Reservoir Storage Forecasting Using Machine Learning." Atmosphere 13, no. 11 (2022): 1887. http://dx.doi.org/10.3390/atmos13111887.

Full text

Abstract:

Storage rate forecasting for the agricultural reservoir is helpful for preemptive responses to disasters such as agricultural drought and planning so as to maintain a stable agricultural water supply. In this study, SVM, RF, and ANN machine learning algorithms were tested to forecast the monthly storage rate of agricultural reservoirs. The storage rate observed over 30 years (1991–2022) was set as a label, and nine datasets for a one- to three-month storage rate forecast were constructed using precipitation and evapotranspiration as features. In all, 70% of the total data was used for training

APA, Harvard, Vancouver, ISO, and other styles

22

Si, Tapas, and Ramkinkar Dutta. "Partial Opposition-Based Particle Swarm Optimizer in Artificial Neural Network Training for Medical Data Classification." International Journal of Information Technology & Decision Making 18, no. 05 (2019): 1717–50. http://dx.doi.org/10.1142/s0219622019500329.

Full text

Abstract:

This paper presents an improved opposition-based Particle Swarm Optimizer (PSO) with partial opposition-based learning. The partial opposition-based learning scheme is a new form of opposition-based learning and it is employed to improve the performance. Nowadays, the artificial neural network (ANN), an important machine learning tool, is used in medicine especially in medical disease diagnosis. ANN training is a complex task and a training algorithm has a significant role in ANN’s performance. Therefore, the proposed algorithm is applied in training of Multi-Layer Feed-Forward Neural Network

APA, Harvard, Vancouver, ISO, and other styles

23

Taghizadeh-Mehrjardi, Ruhollah, Mostafa Emadi, Ali Cherati, Brandon Heung, Amir Mosavi, and Thomas Scholten. "Bio-Inspired Hybridization of Artificial Neural Networks: An Application for Mapping the Spatial Distribution of Soil Texture Fractions." Remote Sensing 13, no. 5 (2021): 1025. http://dx.doi.org/10.3390/rs13051025.

Full text

Abstract:

Soil texture and particle size fractions (PSFs) are a critical characteristic of soil that influences most physical, chemical, and biological properties of soil; furthermore, reliable spatial predictions of PSFs are crucial for agro-ecological modeling. Here, series of hybridized artificial neural network (ANN) models with bio-inspired metaheuristic optimization algorithms such as a genetic algorithm (GA-ANN), particle swarm optimization (PSO-ANN), bat (BAT-ANN), and monarch butterfly optimization (MBO-ANN) algorithms, were built for predicting PSFs for the Mazandaran Province of northern Iran

APA, Harvard, Vancouver, ISO, and other styles

24

Kováč, Szabolcs, German Micha’čonok, Igor Halenár, and Pavel Važan. "Comparison of Heat Demand Prediction Using Wavelet Analysis and Neural Network for a District Heating Network." Energies 14, no. 6 (2021): 1545. http://dx.doi.org/10.3390/en14061545.

Full text

Abstract:

Short-Term Load Prediction (STLP) is an important part of energy planning. STLP is based on the analysis of historical data such as outdoor temperature, heat load, heat consumer configuration, and the seasons. This research aims to forecast heat consumption during the winter heating season. By preprocessing and analyzing the data, we can determine the patterns in the data. The results of the data analysis make it possible to form learning algorithms for an artificial neural network (ANN). The biggest disadvantage of an ANN is the lack of precise guidelines for architectural design. Another dis

APA, Harvard, Vancouver, ISO, and other styles

25

Nikolic, Kostantin P. "Stochastic Search Algorithms for Identification, Optimization, and Training of Artificial Neural Networks." Advances in Artificial Neural Systems 2015 (February 28, 2015): 1–16. http://dx.doi.org/10.1155/2015/931379.

Full text

Abstract:

This paper presents certain stochastic search algorithms (SSA) suitable for effective identification, optimization, and training of artificial neural networks (ANN). The modified algorithm of nonlinear stochastic search (MN-SDS) has been introduced by the author. Its basic objectives are to improve convergence property of the source defined nonlinear stochastic search (N-SDS) method as per Professor Rastrigin. Having in mind vast range of possible algorithms and procedures a so-called method of stochastic direct search (SDS) has been practiced (in the literature is called stochastic local sear

APA, Harvard, Vancouver, ISO, and other styles

26

ABDULLAH, LAZIM, and HERRINI MOHD PAUZI. "AN EFFECTIVE MODEL FOR CARBON DIOXIDE EMISSIONS PREDICTION: COMPARISON OF ARTIFICIAL NEURAL NETWORKS LEARNING ALGORITHMS." International Journal of Computational Intelligence and Applications 13, no. 03 (2014): 1450014. http://dx.doi.org/10.1142/s146902681450014x.

Full text

Abstract:

This paper intends to compare various learning algorithms available for training the multi-layer perceptron (MLP) type of artificial neural networks (ANNs). By using different learning algorithms, this study investigates the performances of gradient descent (GD) algorithm; Levenberg-Marquardt (LM) algorithm; and also Boyden, Fletcher, Goldfarb and Shannon (BFGS) algorithm to predict the emissions of carbon dioxide ( CO 2) in Malaysia. The impact factors of emissions, such as energy use; gross domestic product per capita; population density; combustible renewable and waste; also CO 2 intensity

APA, Harvard, Vancouver, ISO, and other styles

27

Drgan, Viktor, Katja Venko, Janja Sluga, and Marjana Novič. "Merging Counter-Propagation and Back-Propagation Algorithms: Overcoming the Limitations of Counter-Propagation Neural Network Models." International Journal of Molecular Sciences 25, no. 8 (2024): 4156. http://dx.doi.org/10.3390/ijms25084156.

Full text

Abstract:

Artificial neural networks (ANNs) are nowadays applied as the most efficient methods in the majority of machine learning approaches, including data-driven modeling for assessment of the toxicity of chemicals. We developed a combined neural network methodology that can be used in the scope of new approach methodologies (NAMs) assessing chemical or drug toxicity. Here, we present QSAR models for predicting the physical and biochemical properties of molecules of three different datasets: aqueous solubility, acute fish toxicity toward fat head minnow, and bio-concentration factors. A novel neural

APA, Harvard, Vancouver, ISO, and other styles

28

Kisi, Ozgur, Ali Keshavarzi, Jalal Shiri, Mohammad Zounemat-Kermani, and El-Sayed Ewis Omran. "Groundwater quality modeling using neuro-particle swarm optimization and neuro-differential evolution techniques." Hydrology Research 48, no. 6 (2017): 1508–19. http://dx.doi.org/10.2166/nh.2017.206.

Full text

Abstract:

Abstract Recently, the capabilities of artificial neural networks (ANNs) in simulating dynamic systems have been proven. However, the common training algorithms of ANNs (e.g., back-propagation and gradient algorithms) are featured with specific drawbacks in terms of slow convergence and probable entrapment in local minima. Alternatively, novel training techniques, e.g., particle swarm optimization (PSO) and differential evolution (DE) algorithms might be employed for conquering these shortcomings. In this paper, ANN-PSO and ANN-DE models were applied for modeling groundwater qualitative parame

APA, Harvard, Vancouver, ISO, and other styles

29

Aytekin, Mustafa. "Estimating bearing capacity of shallow foundations by artificial neural networks." Challenge Journal of Structural Mechanics 3, no. 4 (2017): 151. http://dx.doi.org/10.20528/cjsmec.2017.11.016.

Full text

Abstract:

In this study, the Artificial Neural Network, ANN is applied to data extracted from a large set of random data created by using Terzaghi and Meyerhof formulae. By using MS Excel, 3750 sets of data for Terzaghi's equation, 4000 for Meyerhof's equation were generated. A simulated ANN was trained on a subset of bearing capacity data, and the performance was tested on the remaining data. The performances of the ANN models were compared to Terzaghi and Meyerhof results. ANN models were as accurate as the other techniques in estimating the ultimate bearing capacity. The models estimated the ultimate

APA, Harvard, Vancouver, ISO, and other styles

30

Massel, Lyudmila V., Olga M. Gerget, Aleksei G. Massel, and Timur G. Mamedov. "The Use of Machine Learning in Situational Management in Relation to the Tasks of the Power Industry." EPJ Web of Conferences 217 (2019): 01010. http://dx.doi.org/10.1051/epjconf/201921701010.

Full text

Abstract:

The article discusses the application possibilities of machine learning methods (artificial neural networks (ANN) and genetic algorithms (GA) to form management actions when applying the concept of situational management for intelligent support of strategic decision-making on the development of energy. At the first stage, the application of ANN to classify extreme situations in the energy sector, to select the most effective management actions (preventive measures) in order to prevent a critical situation from developing into an emergency. Genetic algorithms are proposed to be used to determin

APA, Harvard, Vancouver, ISO, and other styles

31

Nguyen, Thuy-Anh, Hai-Bang Ly, Hai-Van Thi Mai, and Van Quan Tran. "On the Training Algorithms for Artificial Neural Network in Predicting the Shear Strength of Deep Beams." Complexity 2021 (May 22, 2021): 1–18. http://dx.doi.org/10.1155/2021/5548988.

Full text

Abstract:

This study aims to predict the shear strength of reinforced concrete (RC) deep beams based on artificial neural network (ANN) using four training algorithms, namely, Levenberg–Marquardt (ANN-LM), quasi-Newton method (ANN-QN), conjugate gradient (ANN-CG), and gradient descent (ANN-GD). A database containing 106 results of RC deep beam shear strength tests is collected and used to investigate the performance of the four proposed algorithms. The ANN training phase uses 70% of data, randomly taken from the collected dataset, whereas the remaining 30% of data are used for the algorithms’ evaluation

APA, Harvard, Vancouver, ISO, and other styles

32

Yılmaz, Ömer, Adem Alpaslan Altun, and Murat Köklü. "Optimizing the learning process of multi-layer perceptrons using a hybrid algorithm based on MVO and SA." International Journal of Industrial Engineering Computations 13, no. 4 (2022): 617–40. http://dx.doi.org/10.5267/j.ijiec.2022.5.003.

Full text

Abstract:

Artificial neural networks (ANNs) are one of the artificial intelligence techniques used in real-world problems and applications encountered in almost all industries such as education, health, chemistry, food, informatics, logistics, transportation. ANN is widely used in many techniques such as optimization, modelling, classification and forecasting, and many empirical studies have been carried out in areas such as planning, inventory management, maintenance, quality control, econometrics, supply chain management and logistics related to ANN. The most important and just as hard stage of ANNs i

APA, Harvard, Vancouver, ISO, and other styles

33

Andre Setiawan, Yohanes, Yoga Divayana, and Wayan Widiadha. "PERBANDINGAN ALGORITMA SUPERVISED MACHINE LEARNING UNTUK SISTEM PENGHINDARAN HALANGAN PADA ROBOT ASSISTANT UDAYANA 02 (RATNA02)." Jurnal SPEKTRUM 9, no. 3 (2022): 105. http://dx.doi.org/10.24843/spektrum.2022.v09.i03.p12.

Full text

Abstract:

Supervised Machine Learning can make robots smarter by making decisions automatically. This study compares various Supervised Machine Learning algorithms to determine the best algorithm that can be used on Robot Assistant Udayana 02 (RATNA02). The algorithms to be compared are Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Naïve Bayesian (NB), and K-Nearest Neighbor (KNN). Models were created using the TensorFlow and SKLearn libraries. The model is trained using 100.000 data of left sensor, right sensor, front sensor, robot offset, and da

APA, Harvard, Vancouver, ISO, and other styles

34

Aalipour, Mehdi, Bohumil Šťastný, Filip Horký, and Bahman Jabbarian Amiri. "Scaling an Artificial Neural Network-Based Water Quality Index Model from Small to Large Catchments." Water 14, no. 6 (2022): 920. http://dx.doi.org/10.3390/w14060920.

Full text

Abstract:

Scaling models is one of the challenges for water resource planning and management, with the aim of bringing the developed models into practice by applying them to predict water quality and quantity for catchments that lack sufficient data. For this study, we evaluated artificial neural network (ANN) training algorithms to predict the water quality index in a source catchment. Then, multiple linear regression (MLR) models were developed, using the predicted water quality index of the ANN training algorithms and water quality variables, as dependent and independent variables, respectively. The

APA, Harvard, Vancouver, ISO, and other styles

35

Peiris, Amila T., Jeevani Jayasinghe, and Upaka Rathnayake. "Forecasting Wind Power Generation Using Artificial Neural Network: “Pawan Danawi”—A Case Study from Sri Lanka." Journal of Electrical and Computer Engineering 2021 (March 24, 2021): 1–10. http://dx.doi.org/10.1155/2021/5577547.

Full text

Abstract:

Wind power, as a renewable energy resource, has taken much attention of the energy authorities in many countries, as it is used as one of the major energy sources to satisfy the ever-increasing energy demand. However, careful attention is needed in identifying the wind power potential in a particular area due to climate changes. In this sense, forecasting both wind power generation and wind power potential is essential. This paper develops artificial neural network (ANN) models to forecast wind power generation in “Pawan Danawi”, a functioning wind farm in Sri Lanka. Wind speed, wind direction

APA, Harvard, Vancouver, ISO, and other styles

36

Hashim, Mimi Nurzilah, Muhammad Khusairi Osman, Mohammad Nizam Ibrahim, Ahmad Farid Abidin, and Ahmad Asri Abd Samat. "A Comparison Study of Learning Algorithms for Estimating Fault Location." Indonesian Journal of Electrical Engineering and Computer Science 6, no. 2 (2017): 464. http://dx.doi.org/10.11591/ijeecs.v6.i2.pp464-472.

Full text

Abstract:

Fault location is one of the important scheme in power system protection to locate the exact location of disturbance. Nowadays, artificial neural networks (ANNs) are being used significantly to identify exact fault location on transmission lines. Selection of suitable training algorithm is important in analysis of ANN performance. This paper presents a comparative study of various ANN training algorithm to perform fault location scheme in transmission lines. The features selected into ANN is the time of first peak changes in discrete wavelet transform (DWT) signal by using faulted current sign

APA, Harvard, Vancouver, ISO, and other styles

37

Irfan, Sayed Ameenuddin, Firas A. Abdulkareem, Amatalrhman Radman, Gauthier Faugere, and Eswaran Padmanabhan. "Artificial neural network (ANN) modeling for CO2 adsorption on Marcellus Shale." IOP Conference Series: Earth and Environmental Science 1003, no. 1 (2022): 012029. http://dx.doi.org/10.1088/1755-1315/1003/1/012029.

Full text

Abstract:

Abstract In this work, artificial neural network modeling for CO2 adsorption on various types of Marcellus shale samples is studied. The eight shale geometries are investigated for their CO2 adsorption at 298k and up to 50bar pressure utilizing a gravimetric technique and magnetic suspension balance. ANN modelling was applied to investigate three main objectives which are the impact of various training algorithms, various data initiation points, and altered training/validating ratios and number of neurons required for ANN model. The work can provide insightful knowledge linked to the impact of

APA, Harvard, Vancouver, ISO, and other styles

38

Gao, Qing, Qin He Zhang, Shu Peng Su, Jian Hua Zhang, and Rong Yu Ge. "Prediction Models and Generalization Performance Study in Electrical Discharge Machining." Applied Mechanics and Materials 10-12 (December 2007): 677–81. http://dx.doi.org/10.4028/www.scientific.net/amm.10-12.677.

Full text

Abstract:

In the past decade, artificial neural network(ANN) has been applied in Electrical discharge machining(EDM). However, most of them only discuss parameter prediction or optimization result, few tell how to improve generalization performance. In this study, machining process models have been established based on different training algorithms of ANN, namely Levenberg-Marquardt algorithm (LM), Resilient algorithm (RP), Scaled Conjugate Gradient algorithm (SCG) and Quasi-Newton algorithm(BFGS). All models have been trained by same experimental data, checked by another group data, their generalizatio

APA, Harvard, Vancouver, ISO, and other styles

39

Altaye, Aschenaki, Istvan Farkas, and Piroska Víg. "Impacts of Artificial Neural Network Training Algorithms on the Accuracy of PV System Voltage and Current Predictions." European Journal of Energy Research 5, no. 3 (2025): 1–6. https://doi.org/10.24018/ejenergy.2025.5.3.161.

Full text

Abstract:

This study highlights the importance of selecting the appropriate Artificial Neural Network (ANN) training algorithm-based accuracy of prediction capacities in photovoltaic (PV) systems. Accurate PV system performance prediction, particularly output voltage and current, is essential for optimising energy generation and ensuring grid stability. This study evaluates the impact of three ANN training algorithms Levenberg-Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG) on the prediction of PV voltage and current. The algorithms were tested using solar radiation and

APA, Harvard, Vancouver, ISO, and other styles

40

Ali, Ahmed Shakir Ali, and Mustafa Günal. "Artificial Neural Network for Estimation of Local Scour Depth Around Bridge Piers." Archives of Hydro-Engineering and Environmental Mechanics 68, no. 2 (2021): 87–101. http://dx.doi.org/10.2478/heem-2021-0005.

Full text

Abstract:

Abstract Local scour around bridge piers impairs the stability of bridges’ structures. Therefore, a delicate estimation of the local scour depth is vital in designing the bridge piers foundations. In this research, MATLAB software was used to train artificial neural network (ANN) models with four hundred laboratory datasets from different laboratory studies, including five parameters: pier diameter, flow depth flow velocity, critical sediment velocity, sediment particle size, and equilibrium local scour depth. The outcomes present that the ANN model with the Levenberg-Marquardt algorithm and 1

APA, Harvard, Vancouver, ISO, and other styles

41

Neeraj, Singh Yadav, and Kaushal Kumar Dr. "Analysis of Training Functions in a Biometric System." European Journal of Advances in Engineering and Technology 10, no. 11s (2023): 4–9. https://doi.org/10.5281/zenodo.10638473.

Full text

Abstract:

<strong>ABSTRACT</strong> One of the commonly used Biometric methods is Face Classification. Face images are obtained from FEI face database. In this paper, different Training Functions of Neural Network are studied. In this research, a face recognition system is suggested based on feedforward backpropagation neural network Neural Network (FFBPNN) model. Each model is constructed separately with one input layer, 3 hidden layers and one output layer). Four ANN training algorithms (TRAINLM, TRAINBFG, TRAINGDX, and TRAINRP) are used to train each model separately. Performances using each of the t

APA, Harvard, Vancouver, ISO, and other styles

42

Rahaju, Sri Mumpuni Ngesti, April Lia Hananto, Permana Andi Paristiawan, Abdullahi Tanko Mohammed, Anthony Chukwunonso Opia, and Muhammad Idris. "Comparison of Various Prediction Model for Biodiesel Cetane Number using Cascade-Forward Neural Network." Automotive Experiences 6, no. 1 (2023): 4–13. http://dx.doi.org/10.31603/ae.7050.

Full text

Abstract:

Cetane number (CN) is one of the important fuel properties of diesel fuels. It is a measurement of the ignition quality of diesel fuel. Numerous studies have been published to predict the CN of biodiesels. More recently, the utilization of soft computing methods such as artificial neural networks (ANN) has received considerable attention as a prediction tool. However, most studies in the use of ANN for estimating the CN of biodiesels have only used one algorithm to train a small number of datasets. This study aims to predict the CN of 63 biodiesels based on the fatty acid methyl esters (FAME)

APA, Harvard, Vancouver, ISO, and other styles

43

Gao, Minjun, Junhui Meng, Nuo Ma, Moning Li, and Li Liu. "Artificial neural network–based constitutive relation modelling for the laminated fabric used in stratospheric airship." Composites and Advanced Materials 31 (January 2022): 263498332110731. http://dx.doi.org/10.1177/26349833211073146.

Full text

Abstract:

There have been gradually increasing interests in the stratospheric airship (SSA) as a cost-effective alternative to earth orbit satellites for telecommunication and high-resolution earth observation. Lightweight and high strength envelopes are the keys to the design of SSAs as it directly determines the endurance flight performance and loading deformation characteristics of the airship. Typical SSA envelope material is a laminated fabric, which is composed of fabric layer and other functional layers. Compared with conventional composite structures, the laminated fabric has complex nonlinear m

APA, Harvard, Vancouver, ISO, and other styles

44

Faurina, Ruvita, M. Jumli Gazali, and Icha Dwi Aprilia Herani. "OPTIMIZATION OF DISEASE PREDICTION ACCURACY THROUGH ARTIFICIAL NEURAL NETWORK (ANN) ALGORITHMS IN DIAGNESE APPLICATION." Jurnal Teknik Informatika (Jutif) 5, no. 2 (2024): 339–47. https://doi.org/10.52436/1.jutif.2024.5.2.1182.

Full text

Abstract:

This research aims to enhance the accuracy and speed of diagnoses in the Diagnese application by implementing the ANN algorithm for disease prediction. The dataset used for experimentation was featuring binary data types, containing 131 symptoms used to predict 41 types of diseases. The Diagnese application assists patients in identifying diseases and finding suitable specialist doctors based on reported symptoms. To achieve this goal, researchers explored various machine learning algorithms, such as decision trees, SVM, Random Forest, Logistic Regression, and ANN. Through comprehensive analys

APA, Harvard, Vancouver, ISO, and other styles

45

Sufian, Mohd Danish Irfan Mohd, Nur Ashida Salim, Hasmaini Mohamad, and Zuhaila Mat Yasin. "Implementation of artificial intelligence for prediction performance of solar thermal system." International Journal of Power Electronics and Drive Systems (IJPEDS) 13, no. 3 (2022): 1751. http://dx.doi.org/10.11591/ijpeds.v13.i3.pp1751-1760.

Full text

Abstract:

A related input parameter is used in this case study to forecast solar thermal systems (STS) capabilities and to compare which artificial neural network (ANN) algorithms and other artificial intelligence (AI) methods have the most reliable predictor for STS performance. In order to gauge the performance of the STS, this research aims to implement AI for predicting STS performance by comparing the ANN technique with other methods. Three different training algorithms which are Levenberg-Marquardt (LM), scaled conjugate gradient (SCG) and Bayesian regularization (BR) are considered in this resear

APA, Harvard, Vancouver, ISO, and other styles

46

Mohd, Danish Irfan Mohd Sufian, Ashida Salim Nur, Mohamad Hasmaini, and Mat Yasin Zuhaila. "Implementation of artificial intelligence for prediction performance of solar thermal system." International Journal of Power Electronics and Drive Systems (IJPEDS) 13, no. 3 (2022): 1751–60. https://doi.org/10.11591/ijpeds.v13.i3.pp1751-1760.

Full text

Abstract:

A related input parameter is used in this case study to forecast solar thermal systems (STS) capabilities and to compare which artificial neural network (ANN) algorithms and other artificial intelligence (AI) methods have the most reliable predictor for STS performance. In order to gauge the performance of the STS, this research aims to implement AI for predicting STS performance by comparing the ANN technique with other methods. Three different training algorithms which are Levenberg-Marquardt (LM), scaled conjugate gradient (SCG) and Bayesian regularization (BR) are considered in this resear

APA, Harvard, Vancouver, ISO, and other styles

47

Çavuşlu, Mehmet Ali, and Suhap Şahin. "FPGA IMPLEMENTATION OF ANN TRAINING USING LEVENBERG AND MARQUARDT ALGORITHMS." Neural Network World 28, no. 2 (2018): 161–78. http://dx.doi.org/10.14311/nnw.2018.28.010.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Alizadeh, M. J., A. Shabani, and M. R. Kavianpour. "Predicting longitudinal dispersion coefficient using ANN with metaheuristic training algorithms." International Journal of Environmental Science and Technology 14, no. 11 (2017): 2399–410. http://dx.doi.org/10.1007/s13762-017-1307-1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Özdoğan, Hasan, Yiğit Ali Üncü, Mert Şekerci, and Abdullah Kaplan. "A study on the estimations of (n, t) reaction cross-sections at 14.5 MeV by using artificial neural network." Modern Physics Letters A 36, no. 23 (2021): 2150168. http://dx.doi.org/10.1142/s0217732321501686.

Full text

Abstract:

In this paper, calculations of the [Formula: see text] reaction cross-sections at 14.5 MeV have been presented by utilizing artificial neural network algorithms (ANNs). The systematics are based on the account for the non-equilibrium reaction mechanism and the corresponding analytical formulas of the pre-equilibrium exciton model. Experimental results, obtained from the EXFOR database, have been used to train the ANN with the Levenberg–Marquardt (LM) algorithm which is a feed-forward algorithm and is considered one of the well-known and most effective methods in neural networks. The Regression

APA, Harvard, Vancouver, ISO, and other styles

50

Pathan, Munir S., S. M. Pradhan, and T. Palani Selvam. "MACHINE LEARNING ALGORITHMS FOR IDENTIFICATION OF ABNORMAL GLOW CURVES AND ASSOCIATED ABNORMALITY IN CaSO4:DY-BASED PERSONNEL MONITORING DOSIMETERS." Radiation Protection Dosimetry 190, no. 3 (2020): 342–51. http://dx.doi.org/10.1093/rpd/ncaa108.

Full text

Abstract:

Abstract In the present study, machine learning (ML) methods for the identification of abnormal glow curves (GC) of CaSO4:Dy-based thermoluminescence dosimeters in individual monitoring are presented. The classifier algorithms, random forest (RF), artificial neural network (ANN) and support vector machine (SVM) are employed for identifying not only the abnormal glow curve but also the type of abnormality. For the first time, the simplest and computationally efficient algorithm based on RF is presented for GC classifications. About 4000 GCs are used for the training and validation of ML algorit

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!