Relevant bibliographies by topics / Resilient back propagation

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

Academic literature on the topic 'Resilient back propagation'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Resilient back propagation"

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Garkani-Nejad, Zahra, and Behzad Ahmadi-Roudi. "Investigating the role of weight update functions in developing artificial neural network modeling of retention times of furan and phenol derivatives." Canadian Journal of Chemistry 91, no. 4 (2013): 255–62. http://dx.doi.org/10.1139/cjc-2012-0372.

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A quantitative structure−retention relationship study has been carried out on the retention times of 63 furan and phenol derivatives using artificial neural networks (ANNs). First, a large number of descriptors were calculated using HyperChem, Mopac, and Dragon softwares. Then, a suitable number of these descriptors were selected using a multiple linear regression technique. This paper focuses on investigating the role of weight update functions in developing ANNs. Therefore, selected descriptors were used as inputs for ANNs with six different weight update functions including the Levenberg−Ma
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Yang, Fei, Pengdong Gao, and Yongquan Lu. "Evolving Resilient Back-Propagation Algorithm for Energy Efficiency Problem." MATEC Web of Conferences 77 (2016): 06016. http://dx.doi.org/10.1051/matecconf/20167706016.

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Chithambaram, T., and K. Perumal. "Comparative Study: Artificial Neural Networks Training Functions for Brain Tumor Segmentation for MRI Images." Journal of Computational and Theoretical Nanoscience 17, no. 4 (2020): 1831–38. http://dx.doi.org/10.1166/jctn.2020.8448.

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Brain tumor detection from medical images is essential to diagnose earlier and to take decision in treatment planning. Magnetic Resonance Images (MRI) is frequently preferred for detecting brain tumors by the physicians. This paper analyses various Artificial Neural Networks (ANN) training functions for brain tumor segmentation such as Levenberg-Marquardt (LM), Quasi Newton back propagation (QN), Bayesian regularization (BR), Resilient back propagation algorithm (RP) and Scaled conjugate gradient back propagation (SCG). The training algorithms were employed in different sized network for segme
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Mastorocostas, P. A. "Resilient back propagation learning algorithm for recurrent fuzzy neural networks." Electronics Letters 40, no. 1 (2004): 57. http://dx.doi.org/10.1049/el:20040052.

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Ervina, Mertha Endah, Rini Silvi, and Intaniah Ratna Nur Wisisono. "Peramalan Jumlah Penumpang Kereta Api di Indonesia dengan Resilient Back-Propagation (Rprop) Neural Network." Jurnal Matematika "MANTIK" 4, no. 2 (2018): 90–99. http://dx.doi.org/10.15642/mantik.2018.4.2.90-99.

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Train scheduling affects the level of customer satisfaction and profitability of the train service provider. The prediction method of Back-propagation Neural Network (BPNN) has relatively slow convergence. Therefore, this study uses Resilient Back-propagation (Rprop) because it has a more fast convergence and high accuracy. The model produced is a model for Jabodetabek, Java (non-Jabodetabek), Sumatra, and Indonesia. From the results of data analysis conducted, it can be concluded that the performance of neural network model with Resilient Back-propagation (Rprop) formed from training data giv
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Almiani, Muder, Alia Abughazleh, Yaser Jararweh, and Abdul Razaque. "Resilient Back Propagation Neural Network Security Model For Containerized Cloud Computing." Simulation Modelling Practice and Theory 118 (July 2022): 102544. http://dx.doi.org/10.1016/j.simpat.2022.102544.

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SubramanyaNayak, G., and Dayananda Nayak. "Classification of ECG Signals using ANN with Resilient Back Propagation Algorithm." International Journal of Computer Applications 54, no. 6 (2012): 20–23. http://dx.doi.org/10.5120/8570-2294.

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Soni, Khushi. "A Resilient Back Propagation Based Deep Learning Model for Predicting Customer Churn Rate." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 07 (2025): 1–9. https://doi.org/10.55041/ijsrem51231.

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Data Science and Machine Learning are being used extensively for business analytics. One of the major applications happens to be estimating churn and attrition rates. In today’s competitive market landscape, retaining customers is as crucial as acquiring new ones. Churn rate, which measures the proportion of customers who discontinue their relationship with a business over a specific period, is a critical metric for companies across industries. Forecasting churn enables businesses to proactively address customer dissatisfaction and refine their strategies to retain valuable clients. By underst
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Ayyıldız, Mustafa, and Kerim Çetinkaya. "Predictive modeling of geometric shapes of different objects using image processing and an artificial neural network." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 231, no. 6 (2016): 1206–16. http://dx.doi.org/10.1177/0954408916659310.

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In this study, an artificial neural network model was developed to predict the geometric shapes of different objects using image processing. These objects with various sizes and shapes (circle, square, triangle, and rectangle) were used for the experimental process. In order to extract the features of these geometric shapes, morphological features, including the area, perimeter, compactness, elongation, rectangularity, and roundness, were applied. For the artificial neural network modeling, the standard back-propagation algorithm was found to be the optimum choice for training the model. In th
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P., Anil Kumar, and Anuradha B. "Reflectivity Parameter Extraction from RADAR Images Using Back Propagation Algorithms." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (2018): 2795–803. https://doi.org/10.11591/ijece.v8i5.pp2795-2803.

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Pattern recognition has been acknowledged as one of the promising research areas and it has drawn the awareness among many researchers since its existence at the beginning of the nineties. Multilayer Neural networks are used in pattern Recognition and classification based on the features derived from the input patterns. The Reflectivity information extracted from the Doppler Weather Radar (DWR) image helps in identifying the convective cloud type which has a strong relation to the precipitation rate. The reflectivity information is rooted in the DWR image with the help of colors and color bar
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Dissertations / Theses on the topic "Resilient back propagation"

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Melo, Geisla de Albuquerque. "UTILIZAÇÃO DE PROCESSAMENTO DIGITAL DE IMAGENS E REDES NEURAIS ARTIFICIAIS PARA O RECONHECIMENTO DE ÍNDICES DE SEVERIDADE DA FERRUGEM ASIÁTICA DA SOJA." UNIVERSIDADE ESTADUAL DE PONTA GROSSA, 2015. http://tede2.uepg.br/jspui/handle/prefix/129.

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Made available in DSpace on 2017-07-21T14:19:24Z (GMT). No. of bitstreams: 1 Melo, Geisla Albuquerque.pdf: 2986772 bytes, checksum: 02494f1ef68a9df48a1184c0a3e81dce (MD5) Previous issue date: 2015-05-25<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior<br>According to Embrapa (2013), Brazil is the world's second largest soy producer just after the United States. Season after season, the production and planted area in Brazil is growing, however, climatic factors and crop diseases are affecting plantation, preventing further growth, and causing losses to farmers. Asian rust caused
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TUAN, MU HO-YI, and 端木和奕. "Taiwan 4thgeneration stocks with Multiple resilient back-propagation neural models." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/78922808896083689933.

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碩士<br>中華大學<br>資訊管理學系碩士班<br>103<br>Stock market investment and financial management tools .Was Taiwan plople long-term use, and the share price reflect the value of the company in the market. 4th Generation technology in recent years by the impact of stock to produce 4G stocks, the stock vulnerable to introduction of new products fluctuate, How to find tomorrow's ups and downs rules from historical data, The use of artificial intelligence to carry out an objective point of view of data mining elect and future stock price related indicators, and to establish a predictive model to provide decisio
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Chen, Chien-Hsiang, and 陳建翔. "Application of Stock Technical Indicators on Resilient Back-Propagation Forecasting Stock Price-Taking Semiconductor Industry as Example." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/dh3ku7.

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碩士<br>輔仁大學<br>金融與國際企業學系金融碩士班<br>106<br>The purpose of this research is to apply stock technical indicators to the effectiveness of machine learning in predicting stock prices. First of all, this study uses feature variable transformation and variable screening to improve the speed of model learning, and uses a step-by-step test method to find the best hidden layer, and set training stop conditions. Finally, this study chooses Resilient Back-propagation method to build stock price forecasting model, and summarizes the model assessment method established in this study. Finally, the model will us
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Fick, Machteld. "Neurale netwerke as moontlike woordafkappingstegniek vir Afrikaans." Diss., 2002. http://hdl.handle.net/10500/584.

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Text in Afrikaans<br>Summaries in Afrikaans and English<br>In Afrikaans, soos in NederJands en Duits, word saamgestelde woorde aanmekaar geskryf. Nuwe woorde word dus voortdurend geskep deur woorde aanmekaar te haak Dit bemoeilik die proses van woordafkapping tydens teksprosessering, wat deesdae deur rekenaars gedoen word, aangesien die verwysingsbron gedurig verander. Daar bestaan verskeie afkappingsalgoritmes en tegnieke, maar die resultate is onbevredigend. Afrikaanse woorde met korrekte lettergreepverdeling is net die elektroniese weergawe van die handwoordeboek van die Afrikaanse Taa
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Book chapters on the topic "Resilient back propagation"

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Alankar, Bhavya, Nowsheena Yousf, and Shafqat Ul Ahsaan. "Predictive Analytics for Weather Forecasting Using Back Propagation and Resilient Back Propagation Neural Networks." In Advances in Intelligent Systems and Computing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9330-3_10.

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Temurtas, Fevzullah, Nejat Yumusak, Rustu Gunturkun, Hasan Temurtas, and Osman Cerezci. "Elman’s Recurrent Neural Networks Using Resilient Back Propagation for Harmonic Detection." In PRICAI 2004: Trends in Artificial Intelligence. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28633-2_45.

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Das, Raja, and Mohan Kumar Pradhan. "Artificial Neural Network Training Algorithms in Modeling of Radial Overcut in EDM." In Soft Computing Techniques and Applications in Mechanical Engineering. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3035-0.ch006.

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This chapter describes with the comparison of the most used back propagations training algorithms neural networks, mainly Levenberg-Marquardt, conjugate gradient and Resilient back propagation are discussed. In the present study, using radial overcut prediction as illustrations, comparisons are made based on the effectiveness and efficiency of three training algorithms on the networks. Electrical Discharge Machining (EDM), the most traditional non-traditional manufacturing procedures, is growing attraction, due to its not requiring cutting tools and permits machining of hard, brittle, thin and
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Das, Raja, and Mohan Kumar Pradhan. "Artificial Neural Network Training Algorithms in Modeling of Radial Overcut in EDM." In Research Anthology on Artificial Neural Network Applications. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-2408-7.ch015.

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This chapter describes with the comparison of the most used back propagations training algorithms neural networks, mainly Levenberg-Marquardt, conjugate gradient and Resilient back propagation are discussed. In the present study, using radial overcut prediction as illustrations, comparisons are made based on the effectiveness and efficiency of three training algorithms on the networks. Electrical Discharge Machining (EDM), the most traditional non-traditional manufacturing procedures, is growing attraction, due to its not requiring cutting tools and permits machining of hard, brittle, thin and
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Amitab, Khwairakpam, Debdatta Kandar, and Arnab K. Maji. "Speckle Noise Filtering Using Back-Propagation Multi-Layer Perceptron Network in Synthetic Aperture Radar Image." In Research Advances in the Integration of Big Data and Smart Computing. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-8737-0.ch016.

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Synthetic Aperture Radar (SAR) are imaging Radar, it uses electromagnetic radiation to illuminate the scanned surface and produce high resolution images in all-weather condition, day and night. Interference of signals causes noise and degrades the quality of the image, it causes serious difficulty in analyzing the images. Speckle is multiplicative noise that inherently exist in SAR images. Artificial Neural Network (ANN) have the capability of learning and is gaining popularity in SAR image processing. Multi-Layer Perceptron (MLP) is a feed forward artificial neural network model that consists
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Amitab, Khwairakpam, Debdatta Kandar, and Arnab K. Maji. "Speckle Noise Filtering Using Back-Propagation Multi-Layer Perceptron Network in Synthetic Aperture Radar Image." In Deep Learning and Neural Networks. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0414-7.ch028.

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Synthetic Aperture Radar (SAR) are imaging Radar, it uses electromagnetic radiation to illuminate the scanned surface and produce high resolution images in all-weather condition, day and night. Interference of signals causes noise and degrades the quality of the image, it causes serious difficulty in analyzing the images. Speckle is multiplicative noise that inherently exist in SAR images. Artificial Neural Network (ANN) have the capability of learning and is gaining popularity in SAR image processing. Multi-Layer Perceptron (MLP) is a feed forward artificial neural network model that consists
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M., Fawzi, and Ali H. "Resilient Back Propagation Algorithm for Breast Biopsy Classification Based on Artificial Neural Networks." In Computational Intelligence and Modern Heuristics. InTech, 2010. http://dx.doi.org/10.5772/7817.

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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.
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Conference papers on the topic "Resilient back propagation"

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Prasad, Navneel, Rajeshni Singh, and Sunil Pranit Lal. "Comparison of Back Propagation and Resilient Propagation Algorithm for Spam Classification." In 2013 Fifth International Conference on Computational Intelligence, Modelling and Simulation (CIMSim). IEEE, 2013. http://dx.doi.org/10.1109/cimsim.2013.14.

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Halim, Shamimi A., Azlin Ahmad, Norzaidah Md Noh, Mohd Shazuan B. Md Ali Safudin, and Rashidi Ahmad. "A comparative study between standard Back Propagation and Resilient Propagation on snake identification accuracy." In 2011 International Symposium on Information Technology in Medicine and Education (ITME 2011). IEEE, 2011. http://dx.doi.org/10.1109/itime.2011.6132031.

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Wang, Rui, and Yi Jiang. "An Interference-Resilient Relay Beamforming Scheme Inspired by Back-Propagation Algorithm." In 2020 Information Theory and Applications Workshop (ITA). IEEE, 2020. http://dx.doi.org/10.1109/ita50056.2020.9245001.

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Chie, Szu-Lin Su, He-Nian Shou, and Wen-Hsiung Liu. "Resilient back-propagation neural network for approximation weighted geometric dilution of precision." In 2010 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccsit.2010.5563546.

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Qi, Zhang, Xie Xiufen, Liu Guofu, and Liu Bo. "Attenuating the Wheel Speed Sensor Errors Based on Resilient Back Propagation Neural Network." In 2007 8th International Conference on Electronic Measurement and Instruments. IEEE, 2007. http://dx.doi.org/10.1109/icemi.2007.4351247.

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Asawa, K., N. Wadhwa, and S. Agrahari. "Resilient Back Propagation based Yield Prediction of Keratinase from Bacillus Megaterium SN1." In IASTED Technology Conferences 2010. ACTAPRESS, 2010. http://dx.doi.org/10.2316/p.2010.728-009.

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Reddy, L. Rajesh, Preet Patel, and Shah Krupa Rajendra. "Utilization of Resilient Back Propagation Algorithm and Discrete Wavelet Transform for the Differential Protection of Three Phase Power Transformer." In 2020 21st National Power Systems Conference (NPSC). IEEE, 2020. http://dx.doi.org/10.1109/npsc49263.2020.9331861.

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Liu, Tong, Yuelei Wang, and Peng Cai. "The Calculation of the Tube Wall Temperature of Superheater in Boiler Based on the Resilient Back Propagation Artificial Neural Network." In 2008 Fourth International Conference on Natural Computation. IEEE, 2008. http://dx.doi.org/10.1109/icnc.2008.294.

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Scott, Daniel J., and David C. Jensen. "Implementation of Network Optimization and Resiliency Analysis Towards Mission Assurance." In ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/detc2024-142272.

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Abstract Network optimization and resiliency analysis are pivotal domains revealing network functionality, strength, and resilience. Despite their promise, these methodologies often encounter integration limitations, scalability issues, or functional gaps, prompting the need for further development. This paper explores the characteristics of network optimization and resiliency analysis, presenting existing methodologies, discussing their limitations, and proposing a new approach. Our proposed method integrates genetic algorithms (GAs) and weighted degradation analysis to address these shortcom
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