Relevant bibliographies by topics / Backpropagation through time (BPTT)

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

Academic literature on the topic 'Backpropagation through time (BPTT)'

Author: Grafiati
Published: 4 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Backpropagation through time (BPTT)"

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Lady, Silk Moonlight, Riyanto Trilaksono Bambang, Bagus Harianto Bambang, and Faizah Fiqqih. "Implementation of recurrent neural network for the forecasting of USD buy rate against IDR." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 4 (2023): 4567–81. https://doi.org/10.11591/ijece.v13i4.pp4567-4581.

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This study implements a recurrent neural network (RNN) by comparing two RNN network structures, namely Elman and Jordan using the backpropagation through time (BPTT) programming algorithm in the training and forecasting process in foreign exchange forecasting cases. The activation functions used are the linear transfer function, the tan-sigmoid transfer function (Tansig), and the log-sigmoid transfer function (Logsig), which are applied to the hidden and output layers. The application of the activation function results in the log-sigmoid transfer function being the most appropriate activation
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Moonlight, Lady Silk, Bambang Riyanto Trilaksono, Bambang Bagus Harianto, and Fiqqih Faizah. "Implementation of recurrent neural network for the forecasting of USD buy rate against IDR." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 4 (2023): 4567. http://dx.doi.org/10.11591/ijece.v13i4.pp4567-4581.

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<span lang="EN-US">This study implements a recurrent neural network (RNN) by comparing two RNN network structures, namely Elman and Jordan using the backpropagation through time (BPTT) programming algorithm in the training and forecasting process in foreign exchange forecasting cases. The activation functions used are the linear transfer function, the tan-sigmoid transfer function (Tansig), and the log-sigmoid transfer function (Logsig), which are applied to the hidden and output layers. The application of the activation function results in the log-sigmoid transfer function being the mos
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PALANGPOUR, PARVIZ, GANESH K. VENAYAGAMOORTHY, and KEVIN J. DUFFY. "PREDICTION OF ELEPHANT MOVEMENT IN A GAME RESERVE USING NEURAL NETWORKS." New Mathematics and Natural Computation 05, no. 02 (2009): 421–39. http://dx.doi.org/10.1142/s1793005709001404.

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A large number of South Africa's elephants can be found on small wildlife reserves. The large nutritional demands and destructive foraging behavior of elephants can threaten rare species of vegetation. If conservation management is to protect threatened species of vegetation, knowing how long elephants will stay in one area of a reserve as well as which area they will move to next could be useful. The goal of this study was to train a recurrent neural network to predict an elephant herd's next position in the Pongola Game Reserve. Accurate predictions would provide a useful tool in assessing f
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SUKSMONO, ANDRIYAN BAYU, and AKIRA HIROSE. "BEAMFORMING OF ULTRA-WIDEBAND PULSES BY A COMPLEX-VALUED SPATIO-TEMPORAL MULTILAYER NEURAL NETWORK." International Journal of Neural Systems 15, no. 01n02 (2005): 85–91. http://dx.doi.org/10.1142/s0129065705000128.

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We present a neuro-beamformer of ultra-wideband (UWB) pulses employing complex-valued spatio-temporal multilayer neural network, where complex-valued backpropagation through time (CV-BPTT) is used as a learning algorithm. The system performance is evaluated with a UWB monocycle pulse. Simulation results in suppressing multiple UWB interferers and in steering to multiple desired UWB pulses, demonstrates the applicability of the proposed system.
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Tomonaga, Sutashu, Haruo Mizutani, and Kenji Doya. "Training Recurrent Neural Networks with Inherent Missing Data for Wearable Device Applications (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 28 (2025): 29512–13. https://doi.org/10.1609/aaai.v39i28.35307.

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Wearable devices are transforming healthcare by providing continuous, real-time physiological data for monitoring and analysis. However, data often suffer from noise and significant missing values due to operational constraints and user compliance. Traditional approaches address these issues through data imputation during pre-processing, introducing biases and inaccuracies. We propose a novel method enabling Recurrent Neural Networks (RNNs) to inherently handle missing data without imputation. By implementing teacher-forcing during Backpropagation Through Time (BPTT) when data are available an
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Pratheeksha, P., B. M. Pranav, and Nasreen Azra. "Memory Optimization Techniques in Neural Networks: A Review." International Journal of Engineering and Advanced Technology (IJEAT) 10, no. 6 (2021): 44–48. https://doi.org/10.35940/ijeat.F2991.0810621.

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Deep neural networks have been continuously evolving towards larger and more complex models to solve challenging problems in the field of AI. The primary bottleneck that restricts new network architectures is memory consumption. Running or training DNNs heavily relies on the hardware (CPUs, GPUs, or FPGA) which are either inadequate in terms of memory or hard-to-extend. This would further make it difficult to scale. In this paper, we review some of the latest memory footprint reduction techniques which would enable faster low model complexity. Additionally, it improves accuracy by increasing t
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Alzubi, Abdallah, David Lin, Johan Reimann, and Fadi Alsaleem. "G-CTRNN: A Trainable Low-Power Continuous-Time Neural Network for Human Activity Recognition in Healthcare Applications." Applied Sciences 15, no. 13 (2025): 7508. https://doi.org/10.3390/app15137508.

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Continuous-time Recurrent Neural Networks (CTRNNs) are well-suited for modeling temporal dynamics in low-power neuromorphic and analog computing systems, making them promising candidates for edge-based human activity recognition (HAR) in healthcare. However, training CTRNNs remains challenging due to their continuous-time nature and the need to respect physical hardware constraints. In this work, we propose G-CTRNN, a novel gradient-based training framework for analog-friendly CTRNNs designed for embedded healthcare applications. Our method extends Backpropagation Through Time (BPTT) to contin
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Doyle, H. R., and B. Parmanto. "Recurrent Neural Networks for Predicting Outcomes after Liver Transplantation: Representing Temporal Sequence of Clinical Observations." Methods of Information in Medicine 40, no. 05 (2001): 386–91. http://dx.doi.org/10.1055/s-0038-1634197.

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Summary Objectives: This paper investigates a version of recurrent neural network with the backpropagation through time (BPTT) algorithm for predicting liver transplant graft failure based on a time series sequence of clinical observations. The objective is to improve upon the current approaches to liver transplant outcome prediction by developing a more complete model that takes into account not only the preoperative risk assessment, but also the early postoperative history. Methods: A 6-fold cross-validation procedure was used to measure the performance of the networks. The data set was divi
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Zhang, Jianhua, and Yongyue Wang. "Coordinated Frequency Control for Electric Vehicles and a Thermal Power Unit via an Improved Recurrent Neural Network." Energies 18, no. 3 (2025): 533. https://doi.org/10.3390/en18030533.

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With the advancement of intelligent power generation and consumption technologies, an increasing number of renewable energy sources (RESs), smart loads, and electric vehicles (EVs) are being integrated into smart grids. This paper proposes a coordinated frequency control strategy for hybrid power systems with RESs, smart loads, EVs, and a thermal power unit (TPU), in which EVs and the TPU participate in short-term frequency regulation (FR) jointly. All EVs provide FR auxiliary services as controllable loads; specifically, the EV aggregations operate in charging mode when participating in FR. T
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Skaruz, Jarosław. "Database security: combining neural networks and classification approach." Studia Informatica, no. 23 (December 22, 2020): 95–115. http://dx.doi.org/10.34739/si.2019.23.06.

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In the paper we present a new approach based on application of neural networks to detect SQL attacks. SQL attacks are those attacks that take the advantage of using SQL statements to be performed. The problem of detection of this class of attacks is transformed to time series prediction problem. SQL queries are used as a source of events in a protected environment. To differentiate between normal SQL queries and those sent by an attacker, we divide SQL statements into tokens and pass them to our detection system, which predicts the next token, taking into account previously seen tokens. In the
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Dissertations / Theses on the topic "Backpropagation through time (BPTT)"

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He, Fan. "Real-time Process Modelling Based on Big Data Stream Learning." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-35823.

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Most control systems now are assumed to be unchangeable, but this is an ideal situation. In real applications, they are often accompanied with many changes. Some of changes are from environment changes, and some are system requirements. So, the goal of thesis is to model a dynamic adaptive real-time control system process with big data stream. In this way, control system model can adjust itself using example measurements acquired during the operation and give suggestion to next arrival input, which also indicates the accuracy of states under control highly depends on quality of the process mod
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Křepský, Jan. "Rekurentní neuronové sítě v počítačovém vidění." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-237029.

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The thesis concentrates on using recurrent neural networks in computer vision. The theoretical part describes the basic knowledge about artificial neural networks with focus on a recurrent architecture. There are presented some of possible applications of the recurrent neural networks which could be used for a solution of real problems. The practical part concentrates on face recognition from an image sequence using the Elman simple recurrent network. For training there are used the backpropagation and backpropagation through time algorithms.
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Rao, Hui-Ying, and 饒匯盈. "Solving supply chain optimization networks with Backpropagation-Through-Time method." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/90855396836882413520.

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碩士<br>國立高雄第一科技大學<br>運輸倉儲營運所<br>93<br>The enterprise confront that product life cycle declining and real-time satisfied customer in business environment. So enterprises more and more take to supply chain management. The popular of industrialization made output rate and product quality no longer a absolute advantage, the supply chain management also become the top issue. The thesis base on Perea-Lo´pez et al.(2003) MILP model. To design a supply chain model of decision support by MILP model. Using adaptive network of time period solve supply chain model with BPTT method and train network.
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Chang, Hao-Hsiang, and 張浩祥. "Application of Backpropagation Through Time Algorithm with Linearized Inverse Aircraft Model to Aircraft Landing Control." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/34605866282942834342.

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碩士<br>國立海洋大學<br>航運技術研究所<br>89<br>Because of its better adaptability and robustness for unmodeled systems and hardware implementation capability, neural networks have been applied to flight control to increase the flight controller’s adaptation to different environment. Currently, most of the improvements in the Automatic Landing System (ALS) have been on the guidance instruments. By using improvement calculation methods and high accuracy instruments, these systems provide more accurate flight data to the ALS to make the landing more smooth. However, these researches do not include weather fact
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Book chapters on the topic "Backpropagation through time (BPTT)"

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Fujarewicz, Krzysztof, and Adam Galuszka. "Generalized Backpropagation through Time for Continuous Time Neural Networks and Discrete Time Measurements." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24844-6_24.

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Koščák, Juraj, Rudolf Jakša, and Peter Sinčák. "Influence of Number of Neurons in Time Delay Recurrent Networks with Stochastic Weight Update on Backpropagation Through Time." In Advances in Intelligent Systems and Computing. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33227-2_16.

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Xue, Lang, HanWen Liu, Jing Wang, and Hong Qu. "Reasonable Gradients for Online Training Algorithms in Spiking Neural Networks." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240804.

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Spiking neural networks (SNNs) have the potential to simulate sparse and spatio-temporal dynamics observed in biological neurons, making them promising for achieving energy-efficient artificial general intelligence. While backpropagation through time (BPTT) ensures reliable precision for training SNNs, it is hampered by high computation and storage complexity and does not conform to the instantaneous learning mechanism in brains. On the contrary, online training algorithms, which are biologically interpretable, offer low latency and memory efficiency, and are well-suited for on-chip learning applications. However, recent research exhibit a deficiency in the scientific comprehension of online gradients, which leads to certain limitations. To address this issue, we conduct an in-depth analysis of the calculation deviation in chain derivations induced by weight update and find two pivotal factors that affect the accuracy of online gradients: completeness and timeliness. To further enhance the performance of online training leveraging these findings, we propose spatio-temporal online learning (STOL), which substantially ameliorates the accuracy of the online gradients and demonstrates superior computation and memory efficiency. Our experiments on CIFAR-10, CIFAR-100, ImageNet, CIFAR10-DVS, and DVS128-Gesture datasets demonstrate that our method achieves state-of-the-art performance across most of these tasks. Besides, it shows a great improvement compared with existing online training algorithms.
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"7 Backpropagation Through Time Algorithm in Temperature Prediction." In Deep Learning. De Gruyter, 2020. http://dx.doi.org/10.1515/9783110670905-007.

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Mahmoud Sawsan M., Lotfi Ahmad, Sherkat Nasser, Langensiepen Caroline, and Osman Taha. "Echo State Network for Occupancy Prediction and Pattern Mining in Intelligent Environments." In Ambient Intelligence and Smart Environments. IOS Press, 2009. https://doi.org/10.3233/978-1-60750-034-6-474.

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Pattern analysis and prediction of sensory data is becoming an increasing scientific challenge and a massive economical interest supports the need for better pattern mining techniques. The aim of this paper is to investigate efficient mining of useful information from a sensor network representing an ambient intelligence environment. The goal is to extract and predict behavioral patterns of a person in his/her daily activities by analyzing the time series data representing the behaviour of the occupant, generated using occupancy sensors. There are various techniques available for analysis and prediction of a continuous time series signal. However, the occupancy signal is represented by a binary time series where only discrete values of a signal are available. To build the prediction model, recurrent neural networks are investigated. They are proven to be useful tools to solve the difficulties of the temporal relationships of inputs between observations at different time steps, by maintaining internal states that have memory. In this paper, a special form of recurrent neural network, the so-called Echo State Network (ESN) is used in which discrete values of time series can be well processed. Then, a model developed based on ESN is compared with the most popular recurrent neural networks; namely Back Propagation Through Time (BPTT) and Real Time Recurrent Learning (RTRL). The results showed that ESN provides better prediction results compared with BPTT and RTRL. Using ESN, large datasets are learnt in only few minutes or even seconds. It can be concluded that ESN are efficient and valuable tools in binary time series prediction. The results presented in this paper are based on simulated data generated from a simulator representing a person in a 1 bed room flat.
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Rene, Eldon R., Shishir Kumar Behera, and Hung Suck Park. "Predicting Adsorption Behavior in Engineered Floodplain Filtration System Using Backpropagation Neural Networks." In Machine Learning Algorithms for Problem Solving in Computational Applications. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1833-6.ch011.

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Engineered floodplain filtration (EFF) system is an eco-friendly low-cost water treatment process wherein water contaminants can be removed, by adsorption and-or degraded by microorganisms, as the infiltrating water moves from the wastewater treatment plants to the rivers. An artificial neural network (ANN) based approach was used in this study to approximate and interpret the complex input/output relationships, essentially to understand the breakthrough times in EFF. The input parameters to the ANN model were inlet concentration of a pharmaceutical, ibuprofen (ppm) and flow rate (md– 1), and the output parameters were six concentration-time pairs (C, t). These C, t pairs were the times in the breakthrough profile, when 1%, 5%, 25%, 50%, 75%, and 95% of the pollutant was present at the outlet of the system. The most dependable condition for the network was selected by a trial and error approach and by estimating the determination coefficient (R2) value (&gt;0.99) achieved during prediction of the testing set. The proposed ANN model for EFF operation could be used as a potential alternative for knowledge-based models through proper training and testing of variables.
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Buzzanca, Giuseppe. "Music and Neural Networks." In Artificial Neural Networks in Real-Life Applications. IGI Global, 2006. http://dx.doi.org/10.4018/978-1-59140-902-1.ch012.

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This chapter pertains to the research in the field of music and artificial neural networks. The first attempts to codify human musical cognition through artificial neural networks are taken into account as well as recent and more complex techniques that allow computers to learn and recognize musical styles, genres, or even to compose music. Special topics covered are related to the representation of musical language and to the different systems used for solving them, from classic backpropagation networks to self-organizing maps and modular networks. The author hopes that this chapter will disclose some significant information about this emerging but nonetheless important subfield of AI and at the same time increase some interest and allow for a better understanding of this complex field.
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"Backpropagation Through Time and Derivative Adaptive CriticsA Common Framework for Comparison Portions of this chapter were previously published in [4, 7,9, 1214,23]." In Handbook of Learning and Approximate Dynamic Programming. IEEE, 2009. http://dx.doi.org/10.1109/9780470544785.ch15.

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Shubneet, Mr, Anushka Raj Yadav, Paras Mahajan, Partha Chanda, and Atahar Shihab. "DEEP LEARNING FOR DATA SCIENCE: ARCHITECTURES, ALGORITHMS, AND REALWORLD APPLICATIONS." In Artificial Intelligence Technology in Healthcare: Security and Privacy Issues. Iterative International Publishers (IIP), Selfypage Developers Pvt Ltd., 2025. https://doi.org/10.58532/nbennuraith7.

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Deep learning, a specialized subset of machine learning, employs multi-layered neural networks to autonomously learn hierarchical data representations, eliminating the need for manual feature engineering required in traditional machine learning. Key innovations like convolutional neural networks (CNNs) rev olutionized computer vision through spatial hierarchy learning, while recurrent neural networks (RNNs) enabled sequential data processing for time-series and NLP tasks. The backpropagation algorithm remains central to training these models, optimizing weights via gradient descent while leveraging activation functions (e.g., ReLU, Softmax) to introduce non-linearity. Modern frame- works such as TensorFlow and PyTorch democratize implementation through automatic differentiation and GPU acceleration, supporting architectures like Transformers and GANs that dominate 2025’s AI landscape. These advancements power applications ranging from medical image analysis to realtime language translation, with CNNs achieving &gt;98% accuracy in image classification benchmarks and Transformers enabling contextaware chatbots. As deep learning evolves, techniques like mixed-precision training and neuro-symbolic integration address computational and interpretability challenges, solidifying its role in next-generation AI systems [1, 2]
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Zhou, Yarong, and Anping Ran. "Security Supervision System of Agricultural Product Supply Chain Based on IoT Technology." In Advances in Transdisciplinary Engineering. IOS Press, 2025. https://doi.org/10.3233/atde250243.

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The adaptive feature extraction capability of deep learning algorithms and the ability to collect data throughout the entire process of the Internet of Things provide technical support for efficient supervision of agricultural product supply chains. The research aims to build a safety supervision system for agricultural product supply chain through IoT technology, achieve food safety traceability, and improve the risk identification ability and supervision efficiency of agricultural product supply chain. The research adopts a hierarchical holographic modeling method to construct a risk indicator system, identifies agricultural product supply chain risks through backpropagation neural networks, and combines IoT technology for food safety traceability, thereby establishing a complete agricultural product supply chain safety supervision system. The results show that the average time to trace food safety issues before applying intelligent supervision is 15.9 days, while the average time to trace food safety issues after application is 4.2 minutes, with the maximum time spent being only 13.7 minutes, and it can effectively identify risks in the supply chain. The results indicate that the proposed agricultural product supply chain safety supervision system has improved the accuracy of risk identification in the agricultural product supply chain and achieved full traceability from production to consumption. The research results contribute to improving the quality and safety level of agricultural products and enhancing consumers’ trust in agricultural products.
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Conference papers on the topic "Backpropagation through time (BPTT)"

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Lu, Jiajun, Haozhe Zhu, Xiaoyang Zeng, and Chixiao Chen. "ST-BPTT: A Memory-efficient BPTT SNN Training Approach through Gradient-Contribution-Driven Time-Step Selection." In 2024 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE, 2024. https://doi.org/10.1109/biocas61083.2024.10798378.

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Kasac, J., J. Deur, B. Novakovic, and I. Kolmanovsky. "A BPTT-Like Optimal Control Algorithm With Vehicle Dynamics Control Application." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-67319.

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The paper presents a gradient-based numerical algorithm for optimal control of nonlinear multivariable systems with control and state vectors constraints. The algorithm has a backward-in-time recurrent structure similar to the backpropagation-through-time (BPTT) algorithm, which is mostly used as a learning algorithm for dynamic neural networks. This paper presents an enhancement of the basic optimization algorithm. Our enhanced algorithm uses high-order Adams time-discretization schemes instead of the basic Euler discretization method, and a numerical calculation of Jacobians as an alternativ
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Sugavanam, Sujatha, and David Zimmerman. "Asymptotic observer design using backpropagation through time." In 35th Structures, Structural Dynamics, and Materials Conference. American Institute of Aeronautics and Astronautics, 1994. http://dx.doi.org/10.2514/6.1994-1630.

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Ventresca, Mario, and Hamid R. Tizhoosh. "Opposite Transfer Functions and Backpropagation Through Time." In 2007 IEEE Symposium on Foundations of Computational Intelligence. IEEE, 2007. http://dx.doi.org/10.1109/foci.2007.371529.

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Al Kindhi, Berlian, Tri Arief Sardjono, and M. Hery Purnomo. "Prediction of DNA Hepatitis C Virus based on Recurrent Neural Network-Back Propagation Through Time (RNN-BPTT)." In 2019 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation (ICAMIMIA). IEEE, 2019. http://dx.doi.org/10.1109/icamimia47173.2019.9223395.

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Teufel, E., M. Kletting, W. G. Teich, H. J. Pfleiderer, and C. Tarin-Sauer. "Modelling the glucose metabolism with backpropagation through time trained Elman nets." In 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718). IEEE, 2003. http://dx.doi.org/10.1109/nnsp.2003.1318078.

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"Extension of Backpropagation through Time for Segmented-memory Recurrent Neural Networks." In International Conference on Neural Computation Theory and Applications. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0004103804510456.

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Septiawan, Widya Mas, and Sukmawati Nur Endah. "Suitable Recurrent Neural Network for Air Quality Prediction With Backpropagation Through Time." In 2018 2nd International Conference on Informatics and Computational Sciences (ICICoS). IEEE, 2018. http://dx.doi.org/10.1109/icicos.2018.8621720.

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Tang, Hao, and James Glass. "On Training Recurrent Networks with Truncated Backpropagation Through time in Speech Recognition." In 2018 IEEE Spoken Language Technology Workshop (SLT). IEEE, 2018. http://dx.doi.org/10.1109/slt.2018.8639517.

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Drapala, Jaroslaw, and Jerzy Swiatek. "Backpropagation through Time for Learning of Interconnected Neural Networks -- Identification of Complex Systems." In 2008 19th International Conference on Systems Engineering (ICSENG). IEEE, 2008. http://dx.doi.org/10.1109/icseng.2008.84.

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