Relevant bibliographies by topics / Gradient BP based approach

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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 'Gradient BP based approach'

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

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Journal articles on the topic "Gradient BP based approach"

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Ding, Feng, and Xing Ben Han. "Data-Driven Approach for Equipment Reliability Prediction Using Neural Network." Advanced Materials Research 411 (November 2011): 563–66. http://dx.doi.org/10.4028/www.scientific.net/amr.411.563.

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BP neural network based data-driven method is proposed to predict reliability in this paper. The BP neural network prediction using Gradient Descent Method (GDM), Additional Momentum Gradient Descent Method (AMGDM) and Levenberg-Marquardt Method(L-M) based on numerical optimization theory of training algorithm are compared with different neuron number. The proposed approach is validated via age data collected from computer numerical control (CNC) machine tool in the field. The results from the proposed method show that perfect predicting performance is achieved under considering selecting suit
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Chaurasia, Satvik, and R. Shobana. "An Adaptive Growing Pruning Algorithm to Optimize Dynamic Feed Forward Neural Networks for Nonlinear Dynamic System Identification." International Journal of Microsystems and IoT 3, no. 1 (2025): 1519–25. https://doi.org/10.5281/zenodo.15493712.

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Optimizing the structure is very crucial for effective identification and control of any nonlinear system. Optimization leads to a robust and more generalized structure. In this work, an effective adaptive growing-pruning algorithm scheme is proposed to optimize dynamic feed forward structures. The hidden layer of the static FFNN is made dynamic and the weights of the dynamic FFNN are trained using standard Back propagation algorithm. Firstly, the network is grown only when the MSE is found high and increasing. Likewise, unnecessarily neurons are pruned based on low activation variance. The le
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Wang, Youming, and Didi Qing. "Model Predictive Control of Nonlinear System Based on GA-RBP Neural Network and Improved Gradient Descent Method." Complexity 2021 (March 31, 2021): 1–14. http://dx.doi.org/10.1155/2021/6622149.

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A model predictive control (MPC) method based on recursive backpropagation (RBP) neural network and genetic algorithm (GA) is proposed for a class of nonlinear systems with time delays and uncertainties. In the offline modeling stage, a multistep-ahead predictor with GA-RBP neural network is designed, where GA-BP neural network is used as a one-step prediction model and GA is employed to train the initial weights and bias of the BP neural network. The incorporation of GA into RBP can reduce the possibility of the BP neural network falling into a local optimum instead of reaching global optimiz
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Yantsevich, Aleksei V., Veronika V. Shchur, and Sergey A. Usanov. "Oligonucleotide Preparation Approach for Assembly of DNA Synthons." SLAS TECHNOLOGY: Translating Life Sciences Innovation 24, no. 6 (2019): 556–68. http://dx.doi.org/10.1177/2472630319850534.

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An effective oligonucleotide preparation approach for the thermodynamically balanced, inside-out (TBIO) PCR-based assembly of long synthetic DNA molecules (synthons) is described in the current work. We replaced the necessity to purify individual oligonucleotides with just one purification procedure per approximately 500 base pairs (bp) of duplex DNA. So for an enhanced green fluorescent protein (EGFP) gene of 717 bp, we synthesized 24 oligonucleotides with a length of 50 bases and performed just two solid-phase extraction (SPE) purification procedures. It was found that the capacity of ZipTip
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Qin, Xujiang, Qi He, Xin Zhang, and Xiang Yang. "Data Value Assessment in Digital Economy Based on Backpropagation Neural Network Optimized by Genetic Algorithm." Symmetry 17, no. 5 (2025): 761. https://doi.org/10.3390/sym17050761.

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As a new form of economic activity driven by data resources and digital technologies, the digital economy underscores the strategic significance of data as a core production factor. This growing importance necessitates accurate and robust valuation methods. Data valuation poses core modeling challenges due to its nonlinear nature and the instability of neural networks, including gradient vanishing, parameter sensitivity, and slow convergence. To overcome these challenges, this study proposes a genetic algorithm-optimized BP (GA-BP) model, enhancing the efficiency and accuracy of data valuation
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Singh, Garima, and Laxmi Srivastava. "Genetic Algorithm-Based Artificial Neural Network for Voltage Stability Assessment." Advances in Artificial Neural Systems 2011 (July 31, 2011): 1–9. http://dx.doi.org/10.1155/2011/532785.

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With the emerging trend of restructuring in the electric power industry, many transmission lines have been forced to operate at almost their full capacities worldwide. Due to this, more incidents of voltage instability and collapse are being observed throughout the world leading to major system breakdowns. To avoid these undesirable incidents, a fast and accurate estimation of voltage stability margin is required. In this paper, genetic algorithm based back propagation neural network (GABPNN) has been proposed for voltage stability margin estimation which is an indication of the power system's
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Alkawaz, Ali Najem, Jeevan Kanesan, Anis Salwa Mohd Khairuddin, et al. "Training Multilayer Neural Network Based on Optimal Control Theory for Limited Computational Resources." Mathematics 11, no. 3 (2023): 778. http://dx.doi.org/10.3390/math11030778.

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Backpropagation (BP)-based gradient descent is the general approach to train a neural network with a multilayer perceptron. However, BP is inherently slow in learning, and it sometimes traps at local minima, mainly due to a constant learning rate. This pre-fixed learning rate regularly leads the BP network towards an unsuccessful stochastic steepest descent. Therefore, to overcome the limitation of BP, this work addresses an improved method of training the neural network based on optimal control (OC) theory. State equations in optimal control represent the BP neural network’s weights and biase
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Пахомова, Е. А., А. В. Пахомов, and А. В. Щеголев. "BASIS FOR THE PRACTICAL IMPLEMENTATION OF THE BALANCE OF PAYMENT CURVE AS A TASK FOR THE ECONOMIC ENVIRONMENT IN THE RUSSIAN MODIFICATION OF THE TRIPLE HELIX MODEL." Audit and Financial Analysis, no. 01_2022 (March 3, 2022): 11–16. http://dx.doi.org/10.38097/afa.2022.70.84.003.

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Статья посвящена одной из задач экономического окружения в авторской модификации модели тройной спирали в условиях российской экономики – практической реализации кривой платежного баланса BP как компоненты модели IS-LM-BP. Впервые представлен методический подход к основам практической реализации этой компоненты, основанный на принципе предметно-ориентированной декомпозиции, с анализом декомпозиционных компонент методами корреляционно-регрессионного анализа, Крамера, приведенного градиента. The article is devoted to one of the tasks of the economic environment in the author's modification of th
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Yusong, Liu, Su Zhixun, Zhang Bingjie, Gong Xiaoling, and Sang Zhaoyang. "Convergence Analysis of An Improved Extreme Learning Machine Based on Gradient Descent Method." Journal of Applied Computer Science Methods 8, no. 1 (2016): 5–15. http://dx.doi.org/10.1515/jacsm-2016-0001.

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Abstract Extreme learning machine (ELM) is an efficient algorithm, but it requires more hidden nodes than the BP algorithms to reach the matched performance. Recently, an efficient learning algorithm, the upper-layer-solution-unaware algorithm (USUA), is proposed for the single-hidden layer feed-forward neural network. It needs less number of hidden nodes and testing time than ELM. In this paper, we mainly give the theoretical analysis for USUA. Theoretical results show that the error function monotonously decreases in the training procedure, the gradient of the error function with respect to
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Riedel, Jon L., Alice Telka, Andy Bunn, and John J. Clague. "Reconstruction of climate and ecology of Skagit Valley, Washington, from 27.7 to 19.8 ka based on plant and beetle macrofossils." Quaternary Research 106 (October 27, 2021): 94–112. http://dx.doi.org/10.1017/qua.2021.50.

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AbstractGlacial lake sediments exposed at two sites in Skagit Valley, Washington, encase abundant macrofossils dating from 27.7 to 19.8 cal ka BP. At the last glacial maximum (LGM) most of the valley floor was part of a regionally extensive arid boreal (subalpine) forest that periodically included montane and temperate trees and open boreal species such as dwarf birch, northern spikemoss, and heath. We used the modern distribution and climate of 14 species in 12 macrofossil assemblages and a probability density function approach to reconstruct the LGM climate. Median annual precipitation (MAP)
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Dissertations / Theses on the topic "Gradient BP based approach"

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Al-Mudhaf, Ali F. "A feed forward neural network approach for matrix computations." Thesis, Brunel University, 2001. http://bura.brunel.ac.uk/handle/2438/5010.

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A new neural network approach for performing matrix computations is presented. The idea of this approach is to construct a feed-forward neural network (FNN) and then train it by matching a desired set of patterns. The solution of the problem is the converged weight of the FNN. Accordingly, unlike the conventional FNN research that concentrates on external properties (mappings) of the networks, this study concentrates on the internal properties (weights) of the network. The present network is linear and its weights are usually strongly constrained; hence, complicated overlapped network needs to
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Tzimiropoulos, Georgios. "A fast gradient-based approach to image template matching." Thesis, Imperial College London, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.501778.

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Manmontri, Uttachai. "A gradient-based approach to unsupervised signal separation using signal properties." Thesis, Imperial College London, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.442068.

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Pianazzi, Enrico. "A deep reinforcement learning approach based on policy gradient for mobile robot navigation." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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Reinforcement learning is a model-free technique to solve decision-making problems by learning the best behavior to solve a specific task in a given environment. This thesis work focuses on state-of-the-art reinforcement learning methods and their application to mobile robotics navigation and control. Our work is inspired by the recent developments in deep reinforcement learning and from the ever-growing need for complex control and navigation capabilities from autonomous mobile robots. We propose a reinforcement learning controller based on an actor-critic approach to navigate a mobile rob
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Carnevale, Guido. "Control-based Design and Analysis of Gradient-Tracking Algorithms for Distributed Quadratic Optimization." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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In this work we design an iterative distributed optimization algorithm, based on the well-known distributed gradient tracking algorithm. We show the advantages of a system theoretical approach on a distributed optimization framework in which the cost function is given by a sum of quadratic functions. In other words, the main idea of the work consists in seeing the update equation characterizing an iterative optimization algorithm as the dynamics equation of a discrete-time system in which the decision variable plays the state variable role. The goal of the work is to show how system and cont
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Patharlapati, Sai Ram Charan. "Balancing of Network Energy using Observer Approach." Master's thesis, Universitätsbibliothek Chemnitz, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-209453.

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Efficient energy use is primarily for any sensor networks to function for a longer time period. There have been many efficient schemes with various progress levels proposed by many researchers. Yet, there still more improvements are needed. This thesis is an attempt to make wireless sensor networks with further efficient on energy usage in the network with respect to rate of delivery of the messages. In sensor network architecture radio, sensing and actuators have influence over the power consumption in the entire network. While listening as well as transmitting, energy is consumed by the radi
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Cai, Bo-Yin, and 蔡博胤. "A Behavior Fusion Approach Based on Policy Gradient." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/u6ctx3.

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碩士<br>國立中山大學<br>電機工程學系研究所<br>107<br>In this study, we propose a behavioral fusion algorithm based on policy gradient. We use Actor-Critic algorithm to train sub-tasks. After the training is completed, the behavior fusion algorithm proposed in this paper is used for the learning of complex tasks. We can know the state value function of each sub-task in each state by reading the trained sub-task neural network, then calculate the return of each sub-task, and then pass the normalized return to the behavior fusion algorithm as a policy gradient. When reinforced learning is learning a complex task,
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Abdelhamid, Ahmed. "A non-gradient heuristic topology optimization approach using bond-based peridynamic theory." Thesis, 2017. https://dspace.library.uvic.ca//handle/1828/8452.

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Peridynamics (PD), a reformulation of the Classical Continuum Mechanics (CCM), is a new and promising meshless and nonlocal computational method in solid mechanics. To permit discontinuities, the PD integro-differential equation contains spatial integrals and time derivatives. PD can be considered as the continuum version of molecular dynamics. This feature of PD makes it a good candidate for multi-scale analysis of materials. Concurrently, the topology optimization has also been rapidly growing in view of the need to design lightweight and high performance structures. Therefore, this thesis p
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Liu, Chou-Yuan, and 劉洲源. "Fuzzy-Based Approach Combining Improved Gradient and Saliency Maps for Image Resizing." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/6y3fz7.

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碩士<br>國立聯合大學<br>資訊工程學系碩士班<br>104<br>In recent years, many studies have focused on image resizing. In order to retain the main object when the image is resized, most researchers begin with the detection of the main objects in the image. In studying image resizing, most researchers perform object recognition or edge detection to obtain a gradient map or saliency map of an image for image resizing. In order to get better results, in this paper, we propose a new method for improving the gradient map and saliency map. The fuzzy-based approach is then applied to merge these two maps to generate an i
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Guo, Shu-Wei, and 郭書瑋. "A Study of Biomedical Image Segmentation Using Gradient-based Methods and Supervised Learning Approach." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/40671907532195525945.

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博士<br>國立中興大學<br>資訊科學與工程學系<br>102<br>As the development of the technology, computer techniques are used to improve the performance and efficiency of all the fields. In biomedical imaging and photographic processing, image segmentation techniques are important as a preprocess step. Gradient image and thresholding method are efficient and simple techniques in image segmentation. Sobel operator and Otsu’s thresholding method are widely used. However, Otsu’s thresholding may not find a suitable threshold if the size or standard deviation of some group is large. And user may need different threshold
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Books on the topic "Gradient BP based approach"

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Ewald, Schomig, and United States. National Aeronautics and Space Administration., eds. A new approach to mixed H2/H [infinity]-controller synthesis using gradient-based parameter optimization method: Final technical report ... for the period of March 15, 1900 to March 14, 1993. National Aeronautics and Space Administration, 1993.

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Ewald, Schomig, and United States. National Aeronautics and Space Administration., eds. A new approach to mixed H2/H [infinity]-controller synthesis using gradient-based parameter optimization method: Final technical report ... for the period of March 15, 1900 to March 14, 1993. National Aeronautics and Space Administration, 1993.

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Book chapters on the topic "Gradient BP based approach"

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Zhou, Feiyan, and Xiaofeng Zhu. "Alphabet Recognition Based on Scaled Conjugate Gradient BP Algorithm." In Proceedings of the 9th International Symposium on Linear Drives for Industry Applications, Volume 4. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40640-9_3.

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Zhou, Feiyan, and Xiaofeng Zhu. "Alphabet Recognition Based on Scaled Conjugate Gradient BP Algorithm." In Proceedings of the 2nd International Conference on Green Communications and Networks 2012 (GCN 2012): Volume 1. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35419-9_27.

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Jain, Arush, Mani Sachdeva, and Paramita De. "Facial Monitoring Using Gradient Based Approach." In Communications in Computer and Information Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81462-5_19.

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Seix, Bernardo Morcego, Andreu Català Mallofré, and Núria Piera Carreté. "Qualitative approach to gradient based learning algorithms." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-59497-3_212.

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Chatterjee, Subhrasankar, and Debasis Samanta. "A Gradient-Based Approach to Interpreting Visual Encoding Models." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-58181-6_28.

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Duan, Jinan, Jinhai Zhao, Li Xiao, Chuanshu Yang, and Changsheng Li. "A ROP Optimization Approach Based on Improved BP Neural Network PSO." In Advances in Swarm and Computational Intelligence. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20469-7_2.

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Cruttwell, Geoffrey S. H., Bruno Gavranović, Neil Ghani, Paul Wilson, and Fabio Zanasi. "Categorical Foundations of Gradient-Based Learning." In Programming Languages and Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99336-8_1.

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AbstractWe propose a categorical semantics of gradient-based machine learning algorithms in terms of lenses, parametric maps, and reverse derivative categories. This foundation provides a powerful explanatory and unifying framework: it encompasses a variety of gradient descent algorithms such as ADAM, AdaGrad, and Nesterov momentum, as well as a variety of loss functions such as MSE and Softmax cross-entropy, shedding new light on their similarities and differences. Our approach to gradient-based learning has examples generalising beyond the familiar continuous domains (modelled in categories
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Solanki, Mitesh, and Shilpi Gupta. "A Robust Massive MIMO Detection Based on Conjugate Gradient Approach." In Proceedings of First International Conference on Computational Electronics for Wireless Communications. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6246-1_49.

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Bielecka, Marzena, Andrzej Bielecki, Rafał Obuchowicz, and Adam Piórkowski. "Universal Measure for Medical Image Quality Evaluation Based on Gradient Approach." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50423-6_30.

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Dhamodaran, Sasikala, Shafqat Ul Ahsaan, Naheeda Zaib, and Shraddha Jaiswal. "An Optimized Extreme Gradient Boosting Regressor Approach Based Lung Cancer Detection." In Advances in Intelligent Systems Research. Atlantis Press International BV, 2025. https://doi.org/10.2991/978-94-6463-700-7_26.

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Conference papers on the topic "Gradient BP based approach"

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Lei, Xinyi, Sai Li, Zhixia Ding, Le Yang, and Yong Zhang. "Bearing Fault Diagnosis Based on Optimised Stacked Auto-Encoders and Fractional-order Gradient Descent with Momentum BP Neural Network." In 2024 43rd Chinese Control Conference (CCC). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10662313.

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Chen, Yikun. "Training and Algorithm Optimization of Stable Pendulum Motion of Cart Based on BP Neural Network and Gradient Descent Method." In 2024 IEEE 2nd International Conference on Image Processing and Computer Applications (ICIPCA). IEEE, 2024. http://dx.doi.org/10.1109/icipca61593.2024.10709273.

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Jiang, Wenlong, Honghai Fan, Rongyi Ji, et al. "A Steady-State Approach for Surge and Swab Pressures Calculation of Herschel-Buckley Fluids in Directional/Horizontal Wells." In SPE/AAPG Africa Energy and Technology Conference. SPE, 2016. http://dx.doi.org/10.2118/afrc-2565993-ms.

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ABSTRACT Surge and swab pressures have been known as one of the most important factors for formation fracture, lost circulation and well control problems. Previous surge/swab pressures models are mostly based on Bingham plastic (BP) or Power law (PL) fluids, which cannot adequately describe the flow behavior of drilling fluid. This paper presents a new model for computing surge/swab pressures of Herschel-Buckley (HB) fluids in horizontal/directional wells which involves the effect of eccentric annuli. A axial laminar flow model in eccentric annulus is developed using narrow slot flow model wit
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Xing, Xiao-Shuai, Qing-Quan Zhang, Pei-Lin Yang, Li Chao, and Zhao-Yang Chen. "Research on BP algorithm based on conjugate gradient." In 2010 2nd International Conference on Information Science and Engineering (ICISE). IEEE, 2010. http://dx.doi.org/10.1109/icise.2010.5691875.

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Sun, Qiuhong, Xinhang Xu, and Weihong Bi. "Study of Improved BP Algorithm based on Gradient Descent and Numerical Optimization." In International Conference on Information System and Management Engineering. SCITEPRESS - Science and Technology Publications, 2015. http://dx.doi.org/10.5220/0006028404520456.

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Wen, Zhenhua. "Intelligent Clothing Design Approach Based on BP Neural Network." In 2017 International Conference on Robots & Intelligent Systems (ICRIS). IEEE, 2017. http://dx.doi.org/10.1109/icris.2017.52.

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La Cava, William G., and Kourosh Danai. "Model Structure Adaptation: A Gradient-Based Approach." In ASME 2015 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/dscc2015-9658.

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A gradient-based method of symbolic adaptation is introduced for a class of continuous dynamic models. The proposed Model Structure Adaptation Method (MSAM) starts with the first-principles model of the system and adapts its structure after adjusting its individual components in symbolic form. A key contribution of this work is its introduction of the model’s parameter sensitivity as the measure of symbolic changes to the model. This measure, which is essential to defining the structural sensitivity of the model, not only accommodates algebraic evaluation of candidate models in lieu of more co
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Zhang, Lihua, Jiayin Liu, and Jinfeng Gao. "Application of BP Neural Network Based on the Fastest Gradient Descent Algorithm in Data Fitting." In 2023 International Conference on Intelligent Management and Software Engineering (IMSE). IEEE, 2023. http://dx.doi.org/10.1109/imse61332.2023.00019.

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Jinan Duan, Jinhai Zhao, Li Xiao, Chuanshu Yang, and Huinian Chen. "A ROP prediction approach based on improved BP neural network." In 2014 IEEE 3rd International Conference on Cloud Computing and Intelligence Systems (CCIS). IEEE, 2014. http://dx.doi.org/10.1109/ccis.2014.7175818.

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Hu, Liu, and Mei Xie. "A new image segmentation approach based on gradient." In International Conference on Space information Technology, edited by Cheng Wang, Shan Zhong, and Xiulin Hu. SPIE, 2005. http://dx.doi.org/10.1117/12.657937.

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Reports on the topic "Gradient BP based approach"

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Babuska, I., T. Strouboulis, C. S. Upadhyay, and S. K. Gangaraj. Study of Superconvergence by a Computer-Based Approach. Superconvergence of the Gradient in Finite Element Solutions of Laplace's and Poisson's Equations. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada277537.

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Babuska, I., T. Strouboulis, C. S. Upadhyay, and S. K. Gangaraj. Study of Superconvergence by a Computer-Based Approach: Superconvergence of the Gradient of the Displacement, The Strain and Stress in Finite Element Solutions for Plane Elasticity. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada279885.

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Hudson, Austin, Hans Moritz, and Jarod Norton. Sediment mobility, closure depth, and the littoral system – Oregon and Washington coast. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45346.

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Forty years ago, the depth of closure concept was introduced to provide a systematic, process-based approach to evaluate seasonal changes in cross-shore profiles and sediment mobility in the nearshore. This study aims to extend that theory by directly considering wave-asymmetry in the nearshore environment. This technical note introduces a methodology to calculate wave induced dispersal of dredged material placed in nearshore sites and summarizes analyses validating the approach using data from the South Jetty Site at the Mouth of the Columbia River. This investigation highlights the notion of
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Liu, X., Z. Chen, and S. E. Grasby. Using shallow temperature measurements to evaluate thermal flux anomalies in the southern Mount Meager volcanic area, British Columbia, Canada. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/330009.

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Geothermal is a clean and renewable energy resource. However, locating where elevated thermal gradient anomalies exist is a significant challenge when trying to assess potential resource volumes during early exploration of a prospective geothermal area. In this study, we deployed 22 temperature probes in the shallow subsurface along the south flank of the Mount Meager volcanic complex, to measure the transient temperature variation from September 2020 to August 2021. In our data analysis, a novel approach was developed to estimate the near-surface thermal distribution, and a workflow and code
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Jääskeläinen, Emmihenna. Construction of reliable albedo time series. Finnish Meteorological Institute, 2023. http://dx.doi.org/10.35614/isbn.9789523361782.

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A reliable satellite-based black-sky albedo time series is a crucial part of detecting changes in the climate. This thesis studies the solutions to several uncertainties impairing the quality of the black-sky albedo time series. These solutions include creating a long dynamic aerosol optical depth time series for enhancing the removal of atmospheric effects, a method to fill missing data to improve spatial and temporal coverage, and creating a function to correctly model the diurnal variation of melting snow albedo. Mathematical methods are the center pieces of the solutions found in this thes
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Pasupuleti, Murali Krishna. Stochastic Computation for AI: Bayesian Inference, Uncertainty, and Optimization. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv325.

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Abstract: Stochastic computation is a fundamental approach in artificial intelligence (AI) that enables probabilistic reasoning, uncertainty quantification, and robust decision-making in complex environments. This research explores the theoretical foundations, computational techniques, and real-world applications of stochastic methods, focusing on Bayesian inference, Monte Carlo methods, stochastic optimization, and uncertainty-aware AI models. Key topics include probabilistic graphical models, Markov Chain Monte Carlo (MCMC), variational inference, stochastic gradient descent (SGD), and Bayes
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Steffenson, B. J., I. Mayrose, Gary J. Muehlbauer, and A. Sharon. ing and comparative sequence analysis of powdery mildew and leaf rust resistance gene complements in wild barley. United States-Israel Binational Agricultural Research and Development Fund, 2021. http://dx.doi.org/10.32747/2021.8134173.bard.

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Our overall, long-term goal is to exploit the genetic diversity present in cereal wild relatives for the development of cultivars with durable disease resistance. Our specific objectives for this proposal were to: 1) Utilize Association Genetics Resistance Gene Enrichment Sequencing (AgRenSeq) to identify and clone powdery mildew and leaf rust resistance gene complements in wild barley and 2) Conduct comparative sequence analyses of the cloned resistance genes to elucidate the basis of their specificity and evolution. The deployment of resistant cultivars is the most effective, economically ef
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Bhushan, Shanti, Greg Burgreen, Wesley Brewer, and Ian Dettwiller. Assessment of neural network augmented Reynolds averaged Navier Stokes turbulence model in extrapolation modes. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49702.

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A machine-learned model enhances the accuracy of turbulence transport equations of RANS solver and applied for periodic hill test case. The accuracy is investigated in extrapolation modes. A parametric study is also performed to understand the effect of network hyperparameters on training and model accuracy and to quantify the uncertainty in model accuracy due to the non-deterministic nature of the neural network training. For any network, less than optimal mini-batch size results in overfitting, and larger than optimal reduces accuracy. Data clustering is an efficient approach to prevent the
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Ozias-Akins, P., and R. Hovav. molecular dissection of the crop maturation trait in peanut. United States-Israel Binational Agricultural Research and Development Fund, 2020. http://dx.doi.org/10.32747/2020.8134157.bard.

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Crop maturation is one of the most recognized characteristics of peanut, and it is crucial for adaptability and yield. However, not much is known regarding its genetic and molecular control. The goals of this project were to study the molecular-genetic components that control crop maturation in peanut and identify candidate genes. Crop maturation was studied directly by phenotyping the maturity level or through other component traits such as flowering pattern and branching habit. Six different RIL populations (HH, RR, CC, FNC, TGT and FLIC) were used for the genetic analysis. In total, 14 QTLs
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Medrano, Juan, Adam Friedmann, Moshe (Morris) Soller, Ehud Lipkin, and Abraham Korol. High resolution linkage disequilibrium mapping of QTL affecting milk production traits in Israel Holstein dairy cattle. United States Department of Agriculture, 2008. http://dx.doi.org/10.32747/2008.7696509.bard.

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Original objectives: To create BAC contigs covering two QTL containing chromosomal regions (QTLR) and obtain BAC end sequence information as a platform for SNP identification. Use the SNPs to search for marker-QTL linkage disequilibrium (LD) in the test populations (US and Israel Holstein cattle). Identify candidate genes, test for association with dairy cattle production and functional traits, and confirm any associations in a secondary test population. Revisions in the course of the project: The selective recombinant genotyping (SRG) methodology which we implemented to provide moderate resol
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