Relevant bibliographies by topics / ANN-training algorithms

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'ANN-training algorithms'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "ANN-training algorithms"

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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.

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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
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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.

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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,
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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.

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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
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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.

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<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
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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.

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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
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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.

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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
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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.

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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
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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.

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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
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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.

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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
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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.

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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
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Dissertations / Theses on the topic "ANN-training algorithms"

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Rowe, Raymond C., A. P. Plumb, Peter York, and M. Brown. "Optimisation of the predictive ability of artificial neural network (ANN) models: A comparison of three ANN programs and four classes of training algorithm." 2005. http://hdl.handle.net/10454/3011.

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No<br>The purpose of this study was to determine whether artificial neural network (ANN) programs implementing different backpropagation algorithms and default settings are capable of generating equivalent highly predictive models. Three ANN packages were used: INForm, CAD/Chem and MATLAB. Twenty variants of gradient descent, conjugate gradient, quasi-Newton and Bayesian regularisation algorithms were used to train networks containing a single hidden layer of 3¿12 nodes. All INForm and CAD/Chem models trained satisfactorily for tensile strength, disintegration time and percentage dissolutio
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Book chapters on the topic "ANN-training algorithms"

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Kokre, Krishna, and S. V. Jadhav. "ANN Control Algorithms with Different Training Methods as Applied to PMSM Drive." In Algorithms for Intelligent Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1295-4_36.

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Bolón-Canedo, Verónica, Diego Peteiro-Barral, Amparo Alonso-Betanzos, Bertha Guijarro-Berdiñas, and Noelia Sánchez-Maroño. "Scalability Analysis of ANN Training Algorithms with Feature Selection." In Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25274-7_9.

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Ranjan, R., A. Panchal, S. Karpe, and S. Menon. "Machine Learning Strategy for Subgrid Modeling of Turbulent Combustion Using Linear Eddy Mixing Based Tabulation." In Lecture Notes in Energy. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-16248-0_7.

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AbstractThis chapter describes the use of machine learning (ML) algorithms with the linear-eddy mixing (LEM) based tabulation for modeling of subgrid turbulence-chemistry interaction. The focus will be on the use of artificial neural network (ANN), particularly, supervised deep learning (DL) techniques within the finite-rate kinetics framework. We discuss the accuracy and efficiency aspects of two different strategies, where LEM based tabulation is used in both of them. While in the first approach, referred to as LANN-LES, the subgrid reaction-rate term is obtained efficiently using ANN in the
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Noamane, Ncir, Sebbane Saliha, and Nabil El Akchioui. "Comparison of the Efficiency of ANN Training Algorithms for Tracking the Maximum Power Point of Photovoltaic Field." In The Proceedings of the International Conference on Electrical Systems & Automation. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0035-8_2.

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Rivero, Daniel, Julian Dorado, Enrique Fernández-Blanco, and Alejandro Pazos. "A Genetic Algorithm for ANN Design, Training and Simplification." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02478-8_49.

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Czarnowski, Ireneusz, and Piotr Jȩdrzejowicz. "Probability Distribution of Solution Time in ANN Training Using Population Learning Algorithm." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24844-6_21.

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Czarnowski, Ireneusz, and Piotr Jędrzejowicz. "Implementation and Performance Evaluation of the Agent-Based Algorithm for ANN Training." In Agent and Multi-Agent Systems: Technologies and Applications. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72830-6_14.

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Bacanin, Nebojsa, Miodrag Zivkovic, Zlatko Hajdarevic, et al. "Performance of Sine Cosine Algorithm for ANN Tuning and Training for IoT Security." In Hybrid Intelligent Systems. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27409-1_27.

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Çevik, Kerim Kürşat. "Heuristic Approach Performances for Artificial Neural Networks Training." In Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-2742-9.ch019.

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This chapter aimed to evaluate heuristic approach performances for artificial neural networks (ANN) training. For this purpose, software that can perform ANN training application was developed using four different algorithms. First of all, training system was developed via back propagation (BP) algorithm, which is the most commonly used method for ANN training in the literature. Then, in order to compare the performance of this method with the heuristic methods, software that performs ANN training with genetic algorithm (GA), particle swarm optimization (PSO), and artificial immunity (AI) meth
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Karegowda, Asha Gowda, and Devika G. "Meta-Heuristic Parameter Optimization for ANN and Real-Time Applications of ANN." In Research Anthology on Artificial Neural Network Applications. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-2408-7.ch008.

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Artificial neural networks (ANN) are often more suitable for classification problems. Even then, training of ANN is a surviving challenge task for large and high dimensional natured search space problems. These hitches are more for applications that involves process of fine tuning of ANN control parameters: weights and bias. There is no single search and optimization method that suits the weights and bias of ANN for all the problems. The traditional heuristic approach fails because of their poorer convergence speed and chances of ending up with local optima. In this connection, the meta-heuris
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Conference papers on the topic "ANN-training algorithms"

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Felde, Imre. "Computational Heuristics for Prediction of Heat Transfer Characteristics of Quenchants." In IFHTSE 2024. ASM International, 2024. http://dx.doi.org/10.31399/asm.cp.ifhtse2024p0239.

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Abstract Understanding the Heat Transfer Coefficient (HTC) is essential for evaluating cooling media used in the immersion quenching of steels. This HTC characterizes the heat exchange between the immersed workpiece and the quenchant. Calculating the HTC involves solving an inverse heat transfer problem, which typically requires stochastic optimization algorithms. These algorithms use iterative processes and can be computationally demanding, often needing hundreds or thousands of iterations to find a solution. To reduce this computational burden, this paper introduces an initialization techniq
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Alba-Robles, Emilio, Oscar Daniel Lara-Monta�o, Fernando Israel G�mez-Castro, Jahaziel Alberto S�nchez-G�mez, and Manuel Toledano-Ayala. "Modelling of a Propylene Glycol Production Process With Artificial Neural Networks: Optimization of the Architecture." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.139694.

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Chemical process models often involve high non-linearity due to thermodynamic and kinetic relationships, with non-convex bilinear terms adding complexity to process optimization. Recently, data-driven models, particularly artificial neural networks (ANNs), have gained traction for representing chemical processing units. The predictive accuracy of ANNs depends on data quality, variable interactions, and network architecture, the latter being an optimization challenge itself. This study proposes and evaluates two strategies to optimize ANN architecture for modeling a propylene glycol production
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Halder, Anubhav, Jonah Whitt, Farhan Gandhi, and Etana Ferede. "Gross Weight, CG Position, and Airspeed Estimation of Large Multicopters for Advanced Air Mobility." In Vertical Flight Society 81st Annual Forum and Technology Display. The Vertical Flight Society, 2025. https://doi.org/10.4050/f-0081-2025-300.

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This paper presents a comprehensive evaluation of machine learning approaches for real-time operational/ flight parameter estimation in large electric vertical takeoff and landing (eVTOL) vehicles, addressing the challenges of time-varying payloads and atmospheric disturbances in Advanced Air Mobility (AAM) missions. Artificial Neural Networks (ANN), Gaussian Process Regression (GPR), and Support Vector Machines (SVM), are compared for their ability to estimate gross weight (GW), longitudinal center of gravity position (CGx), and airspeed (Ux) using readily available flight control inputs and
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Castaneda, H., and M. Urquidi-Macdonald. "Location of Holidays and Assessment of Level of Cathodic Protection on Underground Pipelines Using AC Impedance and Artificial Neural Networks." In CORROSION 2000. NACE International, 2000. https://doi.org/10.5006/c2000-00768.

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Abstract We addressed a methodology for detecting and locating defects or discontinuities on the outside covering of metal underground pipelines. The pipelines were either cathodically protected or non-cathodically protected. By applying a wide range of AC Impedance signals for different frequencies to a steel coated-pipeline and by measuring its corresponding transfer function under laboratory-simulated real conditions, we can design an algorithm capable of studying the pipeline system and determine a specific pattern for monitoring under simulated “real” conditions. Due to the nature of the
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Dario Baptista, F., Sandy Rodrigues, and Fernando Morgado-Dias. "Performance comparison of ANN training algorithms for classification." In 2013 IEEE International Symposium on Intelligent Signal Processing (WISP). IEEE, 2013. http://dx.doi.org/10.1109/wisp.2013.6657493.

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Shankar, Sonali, Bishal Dey Sarkar, Himanshu Chaurasiya, and Sanjeev Thakur. "Microstrip Antenna's Impedance Analysis Using Different ANN Training Algorithms: Comparative Study." In 2015 International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2015. http://dx.doi.org/10.1109/cicn.2015.311.

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NIELSEN, DEVIN, TYLER LOTT, SOM DUTTA, and JUHYEONG LEE. "ARTIFICIAL NEURAL NETWORK (ANN)-BASED PREDICTIVE TOOL FOR ESTIMATING LIGHTNING DAMAGE IN COMPOSITES." In Thirty-sixth Technical Conference. Destech Publications, Inc., 2021. http://dx.doi.org/10.12783/asc36/35819.

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In this study, three artificial neural network (ANN) models are developed with back propagation (BP) optimization algorithms to predict various lightning damage modes in carbon/epoxy laminates. The proposed ANN models use three input variables associated with lightning waveform parameters (i.e., the peak current amplitude, rising time, and decaying time) to predict fiber damage, matrix damage, and through-thickness damage in the composites. The data used for training and testing the networks was actual lightning damage data collected from peer-reviewed published literature. Various BP training
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Kwon Lee, Sang. "Pattern noise prediction using Artificial Neural Network." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001465.

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In early design stage of tire pattern, it is very useful to predict noise level associated with tire pattern. Artificial neural network (ANN) was used for development of the model for the prediction of tire pattern noise recently. The ANN used supervised training method which extracts the feature applying Gaussian curve fitting to the tread profile spectrum of tire pattern and used it as the input of ANN. This method requests laser scanning for tire pattern of a real tire. In early design, there is no real tire. In this study, the convolutional neural network (CNN) to predict tire pattern nois
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Khasawneh, Mohammad Ali, Mohammad Ahmad Alsheyab, and Haneen Issa Al Akhrass. "Modeling Asphalt Pavement Frictional Properties using Different Machine Learning Algorithms." In The 2nd International Conference on Civil Infrastructure and Construction. Qatar University Press, 2023. http://dx.doi.org/10.29117/cic.2023.0075.

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The objective of this work is to use some machine learning algorithms and test its efficiency in developing models to predict Locked Wheel Skid Trailer (LWST) values from Dynamic Friction Tester (DFT) and Circular Texture Meter (CTM) measurements conducted on asphalt pavement surfaces. For this prediction, three models were developed using DFT measurements at different speeds starting from 20km/h (12.5 mph) up to 64 km/h (40 mph) and then same DFT measurements as combination with Mean Profile Depth (MPD) and the last model used the International Friction Index (IFI) parameters (F60 and SP). Th
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Mu, Yu, and Hong Xia. "A Study on Fault Diagnosis Technology of Nuclear Power Plant Based on Decision Tree." In 18th International Conference on Nuclear Engineering. ASMEDC, 2010. http://dx.doi.org/10.1115/icone18-29510.

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The technology of real-time fault diagnosis for NPP has great significance to improve the safety and economy of reactor. At present, expert system, artificial neural network (ANN) and support vector machine (SVM) algorithms are most widely used in the field of NPP fault diagnosis. According to the shortcomings of expert systems, ANN and SVM, the decision tree algorithm is applied in the field of NPP fault diagnosis in this paper. ID3 and C4.5 are applied separately to learn from training samples which are the typical faults of NPP, and diagnose using the acquired knowledge. Then the diagnostic
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Reports on the topic "ANN-training algorithms"

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Arhin, Stephen, Babin Manandhar, Hamdiat Baba Adam, and Adam Gatiba. Predicting Bus Travel Times in Washington, DC Using Artificial Neural Networks (ANNs). Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.1943.

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Washington, DC is ranked second among cities in terms of highest public transit commuters in the United States, with approximately 9% of the working population using the Washington Metropolitan Area Transit Authority (WMATA) Metrobuses to commute. Deducing accurate travel times of these metrobuses is an important task for transit authorities to provide reliable service to its patrons. This study, using Artificial Neural Networks (ANN), developed prediction models for transit buses to assist decision-makers to improve service quality and patronage. For this study, we used six months of Automati
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