Relevant bibliographies by topics / Error back propagation algorithm

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

Academic literature on the topic 'Error back propagation algorithm'

Author: Grafiati
Published: 28 May 2022
Last updated: 31 July 2025

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Journal articles on the topic "Error back propagation algorithm"

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Singarimbun, Roy Nuary. "Adaptive Moment Estimation To Minimize Square Error In Backpropagation Algorithm." Data Science: Journal of Computing and Applied Informatics 4, no. 1 (2020): 27–46. http://dx.doi.org/10.32734/jocai.v4.i1-1160.

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Back - propagation Neural Network has weaknesses such as errors of gradient descent training slowly of error function, training time is too long and is easy to fall into local optimum. Back - propagation algorithm is one of the artificial neural network training algorithm that has weaknesses such as the convergence of long, over-fitting and easy to get stuck in local optima. Back - propagation is used to minimize errors in each iteration. This paper investigates and evaluates the performance of Adaptive Moment Estimation (ADAM) to minimize the squared error in back - propagation gradient desce
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SUN, Wei-wei. "Adaptive Back-Propagation algorithm with magnified error signals." Journal of Computer Applications 28, no. 8 (2008): 2081–83. http://dx.doi.org/10.3724/sp.j.1087.2008.02081.

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Felix, A. Pyatakovich* Lubov V. Khlivnenko Tatyana I. Yakunchenko Kristina F. Makkonen Olga V. Mevsha. "A COMPARATIVE ANALYSIS OF THE RESULTS OF CONVENTIONAL AND COMBINED METHODS OF TRAINING DIRECT PROPAGATION NEURAL NETWORK IN HEALTHY PERSONS TO DETECTING THE DEGREE OF ACTIVITY OF AN AUTONOMOUS NERVOUS SYSTEM." Indo American Journal of Pharmaceutical Sciences 04, no. 09 (2017): 3075–79. https://doi.org/10.5281/zenodo.910741.

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The article deals with a comparative analysis of the efficiency of an artificial neural network (ANN) trained with the help of algorithm back propagation, and of that trained by combining a back propagation algorithm and a variant of Cauchy stochastic training, in detecting the degree of activity of an autonomous nervous system. For the purposes realization of the project has been developed a biotechnical system, including technical device for input electrophysiological information in mode on-line. To evaluate the clinical effectiveness of the classification, records of interpulse intervals in
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Jiang, Yu Lian. "Natural Gas Load Forecasting Based on Improved Back Propagation Neural Network." Applied Mechanics and Materials 563 (May 2014): 312–15. http://dx.doi.org/10.4028/www.scientific.net/amm.563.312.

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To suit for the condition that the relative error is more popular than the absolute error, and overcome the shortcoming of the traditional Back propagation neural network, this paper proposed an improved Back propagation algorithm with additional momentum item based on the sum of relative error square. The improved algorithm was applied to the example of the natural gas load forecasting, simulations showed that the improved algorithm has faster training speed than the traditional algorithm, and has higher accuracy as while.
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Ororbia, Alexander G., and Ankur Mali. "Biologically Motivated Algorithms for Propagating Local Target Representations." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4651–58. http://dx.doi.org/10.1609/aaai.v33i01.33014651.

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Finding biologically plausible alternatives to back-propagation of errors is a fundamentally important challenge in artificial neural network research. In this paper, we propose a learning algorithm called error-driven Local Representation Alignment (LRA-E), which has strong connections to predictive coding, a theory that offers a mechanistic way of describing neurocomputational machinery. In addition, we propose an improved variant of Difference Target Propagation, another procedure that comes from the same family of algorithms as LRA-E. We compare our procedures to several other biologically
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Oh, Sang-Hoon. "Error back-propagation algorithm for classification of imbalanced data." Neurocomputing 74, no. 6 (2011): 1058–61. http://dx.doi.org/10.1016/j.neucom.2010.11.024.

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Sarkar, Dilip. "Methods to speed up error back-propagation learning algorithm." ACM Computing Surveys 27, no. 4 (1995): 519–44. http://dx.doi.org/10.1145/234782.234785.

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Ma, Chi, Liang Zhao, Xuesong Mei, Hu Shi, and Jun Yang. "Thermal error compensation based on genetic algorithm and artificial neural network of the shaft in the high-speed spindle system." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 231, no. 5 (2016): 753–67. http://dx.doi.org/10.1177/0954405416639893.

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To improve the accuracy, generality and convergence of thermal error compensation model based on traditional neural networks, a genetic algorithm was proposed to optimize the number of the nodes in the hidden layer, the weights and the thresholds of the traditional neural network by considering the shortcomings of the traditional neural networks which converged slowly and was easy to fall into local minima. Subsequently, the grey cluster grouping and statistical correlation analysis were proposed to group temperature variables and select thermal sensitive points. Then, the thermal error models
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Negnevitsky, Michael, and Martin J. Ringrose. "Fuzzy Control of Back-Propagation Training." Journal of Advanced Computational Intelligence and Intelligent Informatics 4, no. 6 (2000): 408–11. http://dx.doi.org/10.20965/jaciii.2000.p0408.

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A fuzzy logic controller for updating training parameters in the error back-propagation algorithm is presented. The controller is based on heuristic rules for speeding up the convergence of training process, incorporating both learning rate and momentum constant changes.
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Kadnár, Milan, Peter Káčer, Marta Harničárová, et al. "Comparison of Linear Regression and Artificial Neural Network Models for the Dimensional Control of the Welded Stamped Steel Arms." Machines 11, no. 3 (2023): 376. http://dx.doi.org/10.3390/machines11030376.

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The production of parts by pressing and subsequent welding is commonly used in the automotive industry. The disadvantage of this method of production is that inaccuracies arising during pressing significantly affect the final dimension of the part. However, this can be corrected by the choice of the technological parameters of the following operation—welding. Suitably designed parameters make it possible to partially eliminate inaccuracies arising during pressing and thus increase the overall applicability of this technology. The paper is focused on the upper arm geometry of a car produced in
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Dissertations / Theses on the topic "Error back propagation algorithm"

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Sargelis, Kęstas. "Klaidos skleidimo atgal algoritmo tyrimai." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2009. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2009~D_20090630_094557-88383.

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Šiame darbe detaliai išanalizuotas klaidos skleidimo atgal algoritmas, atlikti tyrimai. Išsamiai analizuota neuroninių tinklų teorija. Algoritmui taikyti ir analizuoti sistemoje Visual Studio Web Developer 2008 sukurta programa su įvairiais tyrimo metodais, padedančiais ištirti algoritmo daromą klaidą. Taip pat naudotasi Matlab 7.1 sistemos įrankiais neuroniniams tinklams apmokyti. Tyrimo metu analizuotas daugiasluoksnis dirbtinis neuroninis tinklas su vienu paslėptu sluoksniu. Tyrimams naudoti gėlių irisų ir oro taršos duomenys. Atlikti gautų rezultatų palyginimai.<br>The present work provide
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Albarakati, Noor. "FAST NEURAL NETWORK ALGORITHM FOR SOLVING CLASSIFICATION TASKS." VCU Scholars Compass, 2012. http://scholarscompass.vcu.edu/etd/2740.

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Classification is one-out-of several applications in the neural network (NN) world. Multilayer perceptron (MLP) is the common neural network architecture which is used for classification tasks. It is famous for its error back propagation (EBP) algorithm, which opened the new way for solving classification problems given a set of empirical data. In the thesis, we performed experiments by using three different NN structures in order to find the best MLP neural network structure for performing the nonlinear classification of multiclass data sets. A developed learning algorithm used here is the ba
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Зимовець, Т. С. "Інтелектуальна інформаційна технологія комп'ютерного діагностування патології волосся". Master's thesis, Сумський державний університет, 2020. https://essuir.sumdu.edu.ua/handle/123456789/78595.

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Проведено синтез системи підтримки прийняття рішень, яка здатна навчатися з використанням нейромережевої технології. Для чого використовувалася нейронна мережа зворотнього поширення. У роботі проведена оптимізація параметрів стандартного алгоритму навчання нейронної мережі такого типу, що дозволило підвищити точність сформованого нейронно мережевого класифікатора. Програмна реалізація виконувалася з використанням пакета розширення NNToolBox середовища MATLAB 6.5.
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Lowton, Andrew D. "A constructive learning algorithm based on back-propagation." Thesis, Aston University, 1995. http://publications.aston.ac.uk/10663/.

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There are been a resurgence of interest in the neural networks field in recent years, provoked in part by the discovery of the properties of multi-layer networks. This interest has in turn raised questions about the possibility of making neural network behaviour more adaptive by automating some of the processes involved. Prior to these particular questions, the process of determining the parameters and network architecture required to solve a given problem had been a time consuming activity. A number of researchers have attempted to address these issues by automating these processes, concentra
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Xiao, Nancy Y. (Nancy Ying). "Using the modified back-propagation algorithm to perform automated downlink analysis." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/40206.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.<br>Includes bibliographical references (p. 121-122).<br>by Nancy Y. Xiao.<br>M.Eng.
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Civelek, Ferda N. (Ferda Nur). "Temporal Connectionist Expert Systems Using a Temporal Backpropagation Algorithm." Thesis, University of North Texas, 1993. https://digital.library.unt.edu/ark:/67531/metadc278824/.

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Representing time has been considered a general problem for artificial intelligence research for many years. More recently, the question of representing time has become increasingly important in representing human decision making process through connectionist expert systems. Because most human behaviors unfold over time, any attempt to represent expert performance, without considering its temporal nature, can often lead to incorrect results. A temporal feedforward neural network model that can be applied to a number of neural network application areas, including connectionist expert systems, h
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Alejo, Eleuterio Roberto. "Análisis del error en redes neuronales: Corrección de los datos y distribuciones no balanceadas." Doctoral thesis, Universitat Jaume I, 2010. http://hdl.handle.net/10803/10490.

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El problema del desbalance de las clases aparece cuando existen muchos más elementos de una o algunas clases, que de la otra u otras clases (dos o múltiples clases). Esta desproporción en el tamaño de las diferentes clases en un mismo conjunto de datos, puede ocasionar una disminución en la efectividad del clasificación sobre las clases menos representadas. En el caso específico de las redes neuronales artificiales, el desbalance de las clases ocasiona lentitud en la convergencia de las clases minoritarias, lo que se traduce en una pobre capacidad de generalización del clasificador. En este tr
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Sisman, Yilmaz Nuran Arzu. "A Temporal Neuro-fuzzy Approach For Time Series Analysis." Phd thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/570366/index.pdf.

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The subject of this thesis is to develop a temporal neuro-fuzzy system for fore- casting the future behavior of a multivariate time series data. The system has two components combined by means of a system interface. First, a rule extraction method is designed which is named Fuzzy MAR (Multivari- ate Auto-regression). The method produces the temporal relationships between each of the variables and past values of all variables in the multivariate time series system in the form of fuzzy rules. These rules may constitute the rule-base in a fuzzy expert system. Second, a temporal neuro-fuzzy system
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Guan, Xing. "Predict Next Location of Users using Deep Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-263620.

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Predicting the next location of a user has been interesting for both academia and industry. Applications like location-based advertising, traffic planning, intelligent resource allocation as well as in recommendation services are some of the problems that many are interested in solving. Along with the technological advancement and the widespread usage of electronic devices, many location-based records are created. Today, deep learning framework has successfully surpassed many conventional methods in many learning tasks, most known in the areas of image and voice recognition. One of the neural
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Halabian, Faezeh. "An Enhanced Learning for Restricted Hopfield Networks." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42271.

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This research investigates developing a training method for Restricted Hopfield Network (RHN) which is a subcategory of Hopfield Networks. Hopfield Networks are recurrent neural networks proposed in 1982 by John Hopfield. They are useful for different applications such as pattern restoration, pattern completion/generalization, and pattern association. In this study, we propose an enhanced training method for RHN which not only improves the convergence of the training sub-routine, but also is shown to enhance the learning capability of the network. Particularly, after describing the architectur
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Books on the topic "Error back propagation algorithm"

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Lowton, Andrew David. A constructive learning algorithm based on back-propagation. Aston University. Department ofComputer Science and Applied Mathematics, 1995.

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Tandon, Neha. Novel Approach for Drug Discovery Using Neural Network Back Propagation Algorithm. GRIN Verlag GmbH, 2018.

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Intelligent information retrieval using an inductive learning algorithm and a back-propagation neural network. University Microfilms International, 1995.

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Kennedy, Stephen. Compression Mode. Bloomsbury Publishing Inc, 2025. https://doi.org/10.5040/9781501369384.

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This book examines how compression can be understood not only as a digital process enacted through computing, but as a wider economic and political phenomenon that impacts on the ecology of waste, diversity and social inclusivity. Setting out from the linguistic underpinning of visual space it proceeds to the development of the MP3 algorithm and an examination of the ‘waste’itcreates. As it does so it challenges the received wisdom, prevalent in western thought, that human reason and logic enacted through language is uniquely capable of bringing order to chaos. Returning to the idea of a sonic
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Book chapters on the topic "Error back propagation algorithm"

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Latifi, Nasim, and Ali Amiri. "Partial and Random Updating Weights in Error Back Propagation Algorithm." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-27337-7_39.

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Xu, Jia, and Lang Pei. "Air Quality Index Prediction Using Error Back Propagation Algorithm and Improved Particle Swarm Optimization." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65978-7_2.

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Li, Wei, Xin’an Yuan, Jianming Zhao, Xiaokang Yin, and Xiao Li. "Research on Real-time and High-Precision Cracks Inversion Algorithm for ACFM Based on GA-BP Neural Network." In Alternating Current Field Measurement Technique for Detection and Measurement of Cracks in Structures. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-7255-1_1.

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AbstractAlternating current field measurement (ACFM) technology is an emerging nondestructive testing method, which has been used widely in oil industry for detecting and evaluating of surface cracks on structures. It is hard to achieve real-time and high-precision cracks inversion for ACFM based on traditional characteristic signals. In this paper, based on the finite element method (FEM) model of electromagnetic coupling ACFM probe, the energy spectrum and phase threshold determination method is present to obtain the crack characteristic signals in real time. The real-time and high- precisio
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d’ Acierno, Antonio, and Roberto Vaccaro. "A Parallel Implementation of the Back-Propagation of Errors Learning Algorithm on a SIMD Parallel Computer." In ICANN ’93. Springer London, 1993. http://dx.doi.org/10.1007/978-1-4471-2063-6_317.

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Drozda, Martin, Sven Schaust, Sebastian Schildt, and Helena Szczerbicka. "An Error Propagation Algorithm for Ad Hoc Wireless Networks." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03246-2_25.

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Lopes, Noel, and Bernardete Ribeiro. "GPU Implementation of the Multiple Back-Propagation Algorithm." In Intelligent Data Engineering and Automated Learning - IDEAL 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04394-9_55.

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Yoo, Jang-Hee, Jae-Woo Kim, and Jong-Uk Choi. "An Adaptive Training Method of Back-Propagation Algorithm." In Intelligent Systems Third Golden West International Conference. Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-011-7108-3_55.

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Satish Kumar, K., V. V. S. Sasank, K. S. Raghu Praveen, and Y. Krishna Rao. "Multilayer Perceptron Back propagation Algorithm for Predicting Breast Cancer." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5400-1_5.

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Chen, D. S., and R. C. Jain. "A robust back-propagation learning algorithm for function approximation." In Artificial Intelligence Frontiers in Statistics. Springer US, 1993. http://dx.doi.org/10.1007/978-1-4899-4537-2_17.

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Paugam-Moisy, Hélène. "Optimal speedup conditions for a parallel back-propagation algorithm." In Parallel Processing: CONPAR 92—VAPP V. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/3-540-55895-0_474.

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Conference papers on the topic "Error back propagation algorithm"

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Roy, Soumava Kumar, and Crefeda Faviola Rodrigues. "Echo Canceller Using Error Back Propagation Algorithm." In 2014 International Conference on Soft Computing & Machine Intelligence (ISCMI). IEEE, 2014. http://dx.doi.org/10.1109/iscmi.2014.33.

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Makram-Ebeid, Sirat, and Viala. "A rationalized error back-propagation learning algorithm." In International Joint Conference on Neural Networks. IEEE, 1989. http://dx.doi.org/10.1109/ijcnn.1989.118725.

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Kolbusz, Janusz, Pawel Rozycki, Oleksandr Lysenko, and Bogdan M. Wilamowski. "Error Back Propagation Algorithm with Adaptive Learning Rate." In 2019 International Conference on Information and Digital Technologies (IDT). IEEE, 2019. http://dx.doi.org/10.1109/dt.2019.8813440.

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Jin Wang, Hongming Yang, Renjun Zhou, and Sheng Su. "Electric load modeling using ANN with error back propagation algorithm." In 7th IET International Conference on Advances in Power System Control, Operation and Management (APSCOM 2006). IEE, 2006. http://dx.doi.org/10.1049/cp:20062096.

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Rozycki, Pawel, Janusz Kolbusz, Grzegorz Krzos, and Bogdan M. Wilamowski. "Approximation-based Estimation of Learning Rate for Error Back Propagation Algorithm." In 2019 IEEE 23rd International Conference on Intelligent Engineering Systems (INES). IEEE, 2019. http://dx.doi.org/10.1109/ines46365.2019.9109445.

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Albarakati, Noor, and Vojislav Kecman. "Fast neural network algorithm for solving classification tasks: Batch error back-propagation algorithm." In IEEE SOUTHEASTCON 2013. IEEE, 2013. http://dx.doi.org/10.1109/secon.2013.6567409.

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Levy, James J., Ravindra A. Athale, and Michael W. Haney. "Simulation of weight noise in back-propagation architectures." In OSA Annual Meeting. Optica Publishing Group, 1990. http://dx.doi.org/10.1364/oam.1990.mn3.

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The back-propagation algorithm1 has become increasingly popular in the neural-net research community. Various optical implementations have been proposed, with the hope of increased performance by means of the parallelism of optics. Realistic models of optoelectronic implementations must include the effects of noise. Although noise often anneals, increasing the convergence rate, excessive noise can effectively transform any updating algorithm into a random search among weight configurations. For the purpose of evaluating the effects of component noise on the performance of the back-propagation
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Huang, Yudong, Gaoqiang Yang, Rui Hao, and Jianhe Guan. "Research and Improvement on Error Back Propagation Neural Network and Learning Algorithm." In 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013). Atlantis Press, 2013. http://dx.doi.org/10.2991/iccsee.2013.107.

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Algarawi, Maha, Janaki S. Saraswatula, Gyanesh Shah, et al. "Back-Propagation Neural Network-Based Guidance Algorithm for Photo-Magnetic Imaging." In Clinical and Translational Biophotonics. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/translational.2024.jm4a.24.

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Photomagnetic imaging (PMI) employs near-infrared light to irradiate tissue and measures the induced temperature using MR thermometry (MRT). The MRT maps are then converted into absorption maps using a dedicated reconstruction algorithm. Here, we present an AI-based method to directly detect tumor boundaries from these MRT maps and use them as soft-a-priori in the standard PMI algorithm. Tests on phantoms showed a nine-fold image reconstruction acceleration, reducing artifacts by 15%, absorption reconstruction error ~2%.
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Kim Sang-Keun. "Implementation of the recognition system of the Korean stenographic characters by error back propagation algorithm." In Proceedings of 8th International Fuzzy Systems Conference. IEEE, 1999. http://dx.doi.org/10.1109/fuzzy.1999.793105.

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Reports on the topic "Error back propagation algorithm"

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Deller, Jr, Hunt J. R., and S. D. A Simple 'Linearized' Learning Algorithm Which Outperforms Back-Propagation. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada249697.

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detecti
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