Relevant bibliographies by topics / Batch Back-propagation algorithm

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Academic literature on the topic 'Batch Back-propagation algorithm'

Author: Grafiati
Published: 5 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Batch Back-propagation algorithm"

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Al Duais, Mohammed Sarhan, and Fatma Susilawati Mohamad. "Dynamically-adaptive Weight in Batch Back Propagation Algorithm via Dynamic Training Rate for Speedup and Accuracy Training." Journal of Telecommunications and Information Technology 4 (December 20, 2017): 82–89. http://dx.doi.org/10.26636/jtit.2017.113017.

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The main problem of batch back propagation (BBP) algorithm is slow training and there are several parameters need to be adjusted manually, such as learning rate. In addition, the BBP algorithm suffers from saturation training. The objective of this study is to improve the speed up training of the BBP algorithm and to remove the saturation training. The training rate is the most significant parameter for increasing the efficiency of the BBP. In this study, a new dynamic training rate is created to speed the training of the BBP algorithm. The dynamic batch back propagation (DBBPLR) algorithm is pres
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MOHAMMED, SARHAN AL_DUAIS, A.G. AL KHULAIDI ABDUALMAJED, SUSILAWATI. MOHAMAD FATMA, et al. "AUTO-ADAPTIVE THE WEIGHT IN BATCH BACK PROPAGATION ALGORITHM VIA DYNAMIC LEARNING RATE." Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) 42, no. 09 (2023): 131–45. https://doi.org/10.5281/zenodo.8348528.

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<strong>Abstract</strong> Batch back propagation (BBP) algorithm is commonly used in many applications, including robotics, automation, and global positioning systems. The man drawbacks of batch back propagation (BBP) algorithm is slow training, and there are several parameters needs to be adjusted manually, also suffers from saturation training. The objective of this study is to improve the speed uptraining of the BBP algorithm and to remove the saturation training. To overcome these problems, we have created a new dynamic learning rate to escape the local minimum, which enables a faster trai
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Jayakumar, Santhakumar, Sathish Kannan, Poongavanam Ganeshkumar, and U. Mohammed Iqbal. "Reinventing the Trochoidal Toolpath Pattern by Adaptive Rounding Radius Loop Adjustments for Precision and Performance in End Milling Operations." Journal of Manufacturing and Materials Processing 9, no. 6 (2025): 171. https://doi.org/10.3390/jmmp9060171.

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The present work intends to assess the impact of trochoidal toolpath rounding radius loop adjustments on surface roughness, nose radius wear, and resultant cutting force during end milling of AISI D3 steel. Twenty experimental trials have been performed utilizing a face-centered central composite design through a response surface approach. Artificial Neural Network (ANN) models were built to forecast outcomes, utilizing four distinct learning algorithms: the Batch Back Propagation Algorithm (BBP), Quick Propagation Algorithm (QP), Incremental Back Propagation Algorithm (IBP), and Levenberg–Mar
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Zhang, Huisheng, Wei Wu, and Mingchen Yao. "Boundedness and convergence of batch back-propagation algorithm with penalty for feedforward neural networks." Neurocomputing 89 (July 2012): 141–46. http://dx.doi.org/10.1016/j.neucom.2012.02.029.

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Nandani, E. J. K. P., and T. T. S. Vidanapathirana. "Forecasting Paddy Yield in Sri Lanka Using Back-propagation Learning in Artificial Neural Network Model." Journal of the University of Ruhuna 12, no. 2 (2024): 110–20. https://doi.org/10.4038/jur.v12i2.8032.

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Climate change has a direct and indirect impact on food production, and food production depends on the available resources such as the quantity of seeds sown, climate, soil moisture, solar radiation, expected carbon, fertilizers, pesticides, government policies, etc. Paddy yield is one of the major contributors to food production, and there are several studies on forecasting paddy yield production in Sri Lanka using common algorithms in ANNs, this study focused on forecasting the paddy yield in Sri Lanka based on some climate factors using the selected best steepest descent optimizer algorithm
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Saranya, N., and Priya S. Kavi. "Deep Convolutional Neural Network Feed-Forward and Back Propagation (DCNN-FBP) Algorithm for Predicting Heart Disease using Internet of Things." International Journal of Engineering and Advanced Technology (IJEAT) 11, no. 1 (2021): 83–87. https://doi.org/10.35940/ijeat.A3212.1011121.

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In recent years, due to the increasing amounts of data gathered from the medical area, the Internet of Things are majorly developed. But the data gathered are of high volume, velocity, and variety. In the proposed work the heart disease is predicted using wearable devices. To analyze the data efficiently and effectively, Deep Canonical Neural Network Feed-Forward and Back Propagation (DCNN-FBP) algorithm is used. The data are gathered from wearable gadgets and preprocessed by employing normalization. The processed features are analyzed using a deep convolutional neural network. The DCNN-FBP al
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N, Saranya, and Kavi Priya S. "Deep Convolutional Neural Network Feed Forward and Back Prop a gation (DCNN F BP) Algorithm f or Predicting Heart Disease u sing Internet o f Things." International Journal of Engineering and Advanced Technology 11, no. 1 (2021): 283–87. http://dx.doi.org/10.35940/ijeat.a3212.1011121.

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In recent years, due to the increasing amounts of data gathered from the medical area, the Internet of Things are majorly developed. But the data gathered are of high volume, velocity, and variety. In the proposed work the heart disease is predicted using wearable devices. To analyze the data efficiently and effectively, Deep Canonical Neural Network Feed-Forward and Back Propagation (DCNN-FBP) algorithm is used. The data are gathered from wearable gadgets and preprocessed by employing normalization. The processed features are analyzed using a deep convolutional neural network. The DCNN-FBP al
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Mohammed, Sarhan Al_Duais, and Susilawati Mohamad Fatma. "Improved Time Training with Accuracy of Batch Back Propagation Algorithm Via Dynamic Learning Rate and Dynamic Momentum Factor." International Journal of Artificial Intelligence (IJ-AI) 7, no. 4 (2018): 170–78. https://doi.org/10.11591/ijai.v7.i4.pp170-178.

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The man problem of batch back propagation (BBP) algorithm is slow training and there are several parameters needs to be adjusted manually, also suffers from saturation training. The learning rate and momentum factor are significant parameters for increasing the efficiency of the (BBP). In this study, we created a new dynamic function of each learning rate and momentum facor. We present the DBBPLM algorithm, which trains with a dynamic function for each the learning rate and momentum factor. A Sigmoid function used as activation function. The XOR problem, balance, breast cancer and iris dataset
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Al_Duais, Mohammed Sarhan, and Fatma Susilawati Mohamad. "Improved Time Training with Accuracy of Batch Back Propagation Algorithm Via Dynamic Learning Rate and Dynamic Momentum Factor." IAES International Journal of Artificial Intelligence (IJ-AI) 7, no. 4 (2018): 170. http://dx.doi.org/10.11591/ijai.v7.i4.pp170-178.

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&lt;span lang="EN-US"&gt;The man problem of batch back propagation (BBP) algorithm is slow training and there are several parameters needs to be adjusted manually, also suffers from saturation training.&lt;/span&gt;&lt;span lang="EN-US"&gt;The learning rate and momentum factor are significant parameters for increasing the efficiency of the (BBP). In this study, we created a new dynamic function of each learning rate and momentum facor. We present the DBBPLM algorithm, which trains with a dynamic function for each the learning rate and momentum factor.&lt;br /&gt; A Sigmoid function used as act
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Hu, Zhang, and Wei Qin. "Fuzzy Method and Neural Network Model Parallel Implementation of Multi-Layer Neural Network Based on Cloud Computing for Real Time Data Transmission in Large Offshore Platform." Polish Maritime Research 24, s2 (2017): 39–44. http://dx.doi.org/10.1515/pomr-2017-0062.

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Abstract With the rapid development of electronic technology, network technology and cloud computing technology, the current data is increasing in the way of mass, has entered the era of big data. Based on cloud computing clusters, this paper proposes a novel method of parallel implementation of multilayered neural networks based on Map-Reduce. Namely in order to meet the requirements of big data processing, this paper presents an efficient mapping scheme for a fully connected multi-layered neural network, which is trained by using error back propagation (BP) algorithm based on Map-Reduce on c
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Book chapters on the topic "Batch Back-propagation algorithm"

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Benes, Peter Mark, Miroslav Erben, Martin Vesely, Ondrej Liska, and Ivo Bukovsky. "HONU and Supervised Learning Algorithms in Adaptive Feedback Control." In Advances in Computational Intelligence and Robotics. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-5225-0063-6.ch002.

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This chapter is a summarizing study of Higher Order Neural Units featuring the most common learning algorithms for identification and adaptive control of most typical representatives of plants of single-input single-output (SISO) nature in the control engineering field. In particular, the linear neural unit (LNU, i.e., 1st order HONU), quadratic neural unit (QNU, i.e. 2nd order HONU), and cubic neural unit (CNU, i.e. 3rd order HONU) will be shown as adaptive feedback controllers of typical models of linear plants in control including identification and control of plants with input time delays. The investigated and compared learning algorithms for HONU will be the step-by-step Gradient Descent adaptation with the study of known modifications of learning rate for improved convergence, the batch Levenberg-Marquardt algorithm, and the Resilient Back-Propagation algorithm. The theoretical achievements will be summarized and discussed as regards their usability and the real issues of control engineering tasks.
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Conference papers on the topic "Batch Back-propagation algorithm"

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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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Xu, Lei, Lei Hou, Yu Li, Zhenyu Zhu, Jiaquan Liu, and Ting Lei. "A Hybrid PSO-BPNN Model Approach for Crude Oil Pipeline Electrical Energy Consumption Forecasting." In ASME 2020 Pressure Vessels & Piping Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/pvp2020-21227.

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Abstract The electrical energy consumption forecasting for crude oil pipeline is critical in many aspects, such as energy consumption target setting, batch scheduling, unit commitment, etc. For actual crude oil pipelines, the nonlinearity of the sample is strong. The electrical energy consumption of crude pipeline is affected by many parameters, including oil physical property parameter, pipe parameter, station parameter, environmental parameter and operating parameter. At the same time, the whole process has the characteristics of intermittency and complex fluctuations. The above three main r
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