Увійти

Готові списки джерел за темами / Resilient back propagation / Статті в журналах

Щоб переглянути інші типи публікацій з цієї теми, перейдіть за посиланням: Resilient back propagation.

Статті в журналах з теми "Resilient back propagation"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями

Оберіть тип джерела:

Ознайомтеся з топ-50 статей у журналах для дослідження на тему "Resilient back propagation".

Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.

Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.

Переглядайте статті в журналах для різних дисциплін та оформлюйте правильно вашу бібліографію.

1

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.

Повний текст джерела

Анотація:

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

Стилі APA, Harvard, Vancouver, ISO та ін.

2

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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

3

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.

Повний текст джерела

Анотація:

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

Стилі APA, Harvard, Vancouver, ISO та ін.

4

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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

5

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.

Повний текст джерела

Анотація:

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

Стилі APA, Harvard, Vancouver, ISO та ін.

6

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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

7

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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

8

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.

Повний текст джерела

Анотація:

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

Стилі APA, Harvard, Vancouver, ISO та ін.

9

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.

Повний текст джерела

Анотація:

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

Стилі APA, Harvard, Vancouver, ISO та ін.

10

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.

Повний текст джерела

Анотація:

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

Стилі APA, Harvard, Vancouver, ISO та ін.

11

Jyotshna, Dongardive, and Abraham Siby. "BRAIN Journal - Secondary Structure Prediction of Protein using Resilient Back Propagation Learning Algorithm." BRAIN - Broad Research in Artificial Intelligence and Neuroscience 6, no. 1-2 (2015): 22–29. https://doi.org/10.5281/zenodo.1044169.

Повний текст джерела

Анотація:

ABSTRACT The paper proposes a neural network based approach to predict secondary structure of protein. It uses Multilayer Feed Forward Network (MLFN) with resilient back propagation as the learning algorithm. Point Accepted Mutation (PAM) is adopted as the encoding scheme and CB396 data set is used for the training and testing of the network. Overall accuracy of the network has been experimentally calculated with different window sizes for the sliding window scheme and by varying the number of units in the hidden layer. The best results were obtained with eleven as the window size and seven as

Стилі APA, Harvard, Vancouver, ISO та ін.

12

S, Rafeek Ahmed. "Smart IOT based Short Term Forecasting of Power Generation Systems and Quality Improvement Using Resilient Back Propagation Neural Network." Revista Gestão Inovação e Tecnologias 11, no. 3 (2021): 1200–1211. http://dx.doi.org/10.47059/revistageintec.v11i3.2004.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

13

Huang, Kai, and Cheng Wei Zhong. "Lightweight Concrete Strength Prediction by BP-ANN." Advanced Materials Research 1090 (February 2015): 101–6. http://dx.doi.org/10.4028/www.scientific.net/amr.1090.101.

Повний текст джерела

Анотація:

The back propagation artificial neural networks (BP-ANN) use a resilient back-propagation algorithm and early stopping technique. By inputing the properties of geometries and material, NNs can predict the strength of lightweight concrete. An BP-ANN model based on feed-forward neural network is built, trained and tested using the available test data of 148 mix records collected from the technical literature. And the test results are compared and analyzed with experimental data . It shows that the strength of lightweight concrete obtained by the simplified model based on NNs are in good agreemen

Стилі APA, Harvard, Vancouver, ISO та ін.

14

Hassan, Musavir, Muheet Ahmad Butt, and Majid Zaman Baba. "Logistic Regression Versus Neural Networks: The Best Accuracy in Prediction of Diabetes Disease." Asian Journal of Computer Science and Technology 6, no. 2 (2017): 33–42. http://dx.doi.org/10.51983/ajcst-2017.6.2.1782.

Повний текст джерела

Анотація:

To derive actionable insights from vast amount of data in an intelligent fashion some techniques are used called machine learning techniques. These techniques support for predicting disease with correct case of training and testing. To classify the medical data logistic regression and artificial neural networks are the models to be selected. Today in world a major health problem is Diabetes Mellitus for which many classification algorithms have been applied for its diagnoses and treatment. To detect diabetes disease in early stage it needs greatest support of machine learning, since it cannot

Стилі APA, Harvard, Vancouver, ISO та ін.

15

Kumar, P. Anil, and B. Anuradha. "Reflectivity Parameter Extraction from RADAR Images Using Back Propagation Algorithm." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (2018): 2795. http://dx.doi.org/10.11591/ijece.v8i5.pp2795-2803.

Повний текст джерела

Анотація:

<p class="Abstract">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 hel

Стилі APA, Harvard, Vancouver, ISO та ін.

16

James, T. O., S. U. Gulumbe, and A. Danbaba. "Resilient Back-Propagation Algorithm in the Prediction of Mother to Child Transmission of HIV." OALib 05, no. 05 (2018): 1–7. http://dx.doi.org/10.4236/oalib.1104538.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

17

Dai, Hao, Qin Xu, Yi Xiong, Wei-Lin Liu, and Dong-Qing Wei. "Improved Prediction of Michaelis Constants in CYP450-Mediated Reactions by Resilient Back Propagation Algorithm." Current Drug Metabolism 17, no. 7 (2016): 673–80. http://dx.doi.org/10.2174/1389200217666160513144551.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

18

Rathnam, S. Muni, and T. Ramashri. "Identification of Volcano Hotspots by using Resilient Back Propagation (RBP) Algorithm Via Satellite Images." i-manager's Journal on Image Processing 1, no. 4 (2014): 1–7. http://dx.doi.org/10.26634/jip.1.4.3034.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

19

Ayyildiz, Mustafa. "Modeling for prediction of surface roughness in milling medium density fiberboard with a parallel robot." Sensor Review 39, no. 5 (2019): 716–23. http://dx.doi.org/10.1108/sr-02-2019-0051.

Повний текст джерела

Анотація:

Purpose This paper aims to discuss the utilization of artificial neural networks (ANNs) and multiple regression method for estimating surface roughness in milling medium density fiberboard (MDF) material with a parallel robot. Design/methodology/approach In ANN modeling, performance parameters such as root mean square error, mean error percentage, mean square error and correlation coefficients (R2) for the experimental data were determined based on conjugate gradient back propagation, Levenberg–Marquardt (LM), resilient back propagation, scaled conjugate gradient and quasi-Newton back propagat

Стилі APA, Harvard, Vancouver, ISO та ін.

20

Lav, Singh Mathur* Mr. Amit Agrawal Dr. Dharmendra Kumar Singh (member of IEI). "MODELING OF BREAKDOWN VOLTAGE OF SOLID INSULATING MATERIALS BY ARTIFICIAL NEURAL NETWORK." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 6 (2016): 788–96. https://doi.org/10.5281/zenodo.56011.

Повний текст джерела

Анотація:

This paper presents a model to find out the breakdown voltage of solid insulating materials under AC excitation condition by employing the artificial neural network method. The paper gives a brief introduction to multilayer perceptrons and resilient back-propagation. A relation between input variables and output variables i. e. breakdown voltage is demonstrated. The inputs to the neural networks are the thickness of material, diameter of void, depth of void and permittivity of materials. Neural network methodology is the one of the most popular and widely used method for the analysis of voids.

Стилі APA, Harvard, Vancouver, ISO та ін.

21

Maibam, Sanju Meetei1. "QUANTIFICATION OF METHANE (MARSH) GAS USING RESILIENT BACKPROPAGATION NEURAL NETWORK." Multilogic in science XIII, no. XXXXVI (2023): 709–12. https://doi.org/10.5281/zenodo.7869736.

Повний текст джерела

Анотація:

This study unequivocally demonstrates that robust propagation neural networks can be used to quantify marsh or methane gases. Methane has lower explosive limit (LEL) is 5.0% (5000 ppm) and its upper explosive limit is 15.0% (150000 ppm) by volume with air. The main purposed of this study is to classify the concentration level below the LEL of the methane gas present in the air by using the resilient back-propagation neural network to prevent the various hazardous caused by the methane.   After the suggested network was trained with defaults free parameters, it provided a very high qu

Стилі APA, Harvard, Vancouver, ISO та ін.

22

Zhao, Ying, and Xiao Ji Chen. "Prediction Model on Urban Residential Water Based on Resilient BP Learning Algorithm." Applied Mechanics and Materials 543-547 (March 2014): 4086–89. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.4086.

Повний текст джерела

Анотація:

Urban water is an important part of urban water systems, Predicting urban residents water is the important work to ensure urban people's lives and to achieve urban water resources supply and demand balance. For BP algorithm existing problems in the training process, this paper uses resilient BP algorithm to improve it. Firstly, analysis of the impact factors of urban residents water for adjusting the prediction model; Then, the study on resilient Back Propagation algorithm, to overcome the adverse effects of gradient amplitude; Finally, the study identifies number of hidden nodes, dynamically

Стилі APA, Harvard, Vancouver, ISO та ін.

23

Salmasi, Mehrshad, and Homayoun Mahdavi-Nasab. "Evaluating the Performance of Training Algorithms in Active Noise Control Using MLP Neural Network." Advanced Materials Research 468-471 (February 2012): 1613–17. http://dx.doi.org/10.4028/www.scientific.net/amr.468-471.1613.

Повний текст джерела

Анотація:

Passive methods are costly and ineffective in noise reduction at low frequencies. Active noise control has been suggested because of these problems. Active noise control (ANC) is based on the destructive interference between the noise source waves and a controlled secondary source. In this paper, various training algorithms are compared in active cancellation of modeled sound noise using MLP neural network. Colored noise signals are used as a model of sound noise instead of noise signals from databases. An MLP neural network with different architectures is used in simulation procedure. The eff

Стилі APA, Harvard, Vancouver, ISO та ін.

24

Xu, Sheng, Yanjing Li, Teli Ma, et al. "Resilient Binary Neural Network." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (2023): 10620–28. http://dx.doi.org/10.1609/aaai.v37i9.26261.

Повний текст джерела

Анотація:

Binary neural networks (BNNs) have received ever-increasing popularity for their great capability of reducing storage burden as well as quickening inference time. However, there is a severe performance drop compared with {real-valued} networks, due to its intrinsic frequent weight oscillation during training. In this paper, we introduce a Resilient Binary Neural Network (ReBNN) to mitigate the frequent oscillation for better BNNs' training. We identify that the weight oscillation mainly stems from the non-parametric scaling factor. To address this issue, we propose to parameterize the scaling

Стилі APA, Harvard, Vancouver, ISO та ін.

25

Mistry, Shivangi, and Falguni Parekh. "Flood Forecasting Using Artificial Neural Network." IOP Conference Series: Earth and Environmental Science 1086, no. 1 (2022): 012036. http://dx.doi.org/10.1088/1755-1315/1086/1/012036.

Повний текст джерела

Анотація:

Abstract The process of assessing the timing, amount, and period of flood events based on observed features of a river basin is known as flood forecasting. Floods cause lots of damage to properties and create a risk to human life. Flood forecasting is critical for developing appropriate flood risk management strategies, reducing flood hazards, evacuating people from flood-prone areas. The main objective of this study is to apply artificial neural networks for forecasting of river flow in the Deo River, located in Gujarat. Rainfall and discharge are the parameters considered for model developme

Стилі APA, Harvard, Vancouver, ISO та ін.

26

Chang, Fangle, and Paul Heinemann. "Prediction of Human Responses to Dairy Odor Using an Electronic Nose and Neural Networks." Transactions of the ASABE 61, no. 2 (2018): 399–409. http://dx.doi.org/10.13031/trans.12177.

Повний текст джерела

Анотація:

Abstract. Odor emitted from dairy operations may cause negative reactions by farm neighbors. Identification and evaluation of such malodors is vital for better understanding of human response and methods for mitigating effects of odors. The human nose is a valuable tool for odor assessment, but using human panels can be costly and time-consuming, and human evaluation of odor is subjective. Sensing devices, such as an electronic nose, have been widely used to measure volatile emissions from different materials. The challenge, though, is connecting human assessment of odors with the quantitative

Стилі APA, Harvard, Vancouver, ISO та ін.

27

Hao, Zhiyong, Chenhao Yu, Junyi Zhu, and Leilei Chang. "High Resilient Asymmetry and Anomaly Detection Based on Data Causality." Symmetry 16, no. 7 (2024): 819. http://dx.doi.org/10.3390/sym16070819.

Повний текст джерела

Анотація:

In the tunnel construction practice, multiple buildings’ tilt rate data are collected. In this study, data causality is defined to reflect the causal relation between the input and output of the building tilt rate detection data. Upon defining and calculating the data causality, a new high resilient causality detection (HiReCau) method is proposed for abnormal building tilt rate detection. A numerical case and another practical case are studied for validation purposes. The case study results show that the proposed HiReCau method can accurately detect high-causality data and low-causality data

Стилі APA, Harvard, Vancouver, ISO та ін.

28

Erkaymaz, Okan. "Resilient back-propagation approach in small-world feed-forward neural network topology based on Newman–Watts algorithm." Neural Computing and Applications 32, no. 20 (2020): 16279–89. http://dx.doi.org/10.1007/s00521-020-05161-6.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

29

Karraz, George. "Develop an Intelligent Anomaly Intrusion Detection System in Computer Networks based on Resilient Back-propagation Neural Network." Damascus University Journal for Basic Sciences, no. 10566-196 (April 29, 2024): 1–18. https://doi.org/10.5281/zenodo.11087938.

Повний текст джерела

Анотація:

Various anomaly attacks and disruptions to information networks are considered serious problems that affect the protection of information exchanged between these networks and affect the maintenance of reliability and confidentiality of information exchange. In the past decade, researchers around the world have faced many challenges and need to propose a set of systems with flexible architectures to accurately and automatically detect anomaly intrusion attacks to address their complexity. Related research has proposed many full-scale solutions based on machine

Стилі APA, Harvard, Vancouver, ISO та ін.

30

Katip, Aslıhan, and Asifa Anwar. "Modeling the Influence of Climate Change on the Water Quality of Doğancı Dam in Bursa, Turkey, Using Artificial Neural Networks." Water 17, no. 5 (2025): 728. https://doi.org/10.3390/w17050728.

Повний текст джерела

Анотація:

Population growth, industrialization, excessive energy consumption, and deforestation have led to climate change and affected water resources like dams intended for public drinking water. Meteorological parameters could be used to understand these effects better to anticipate the water quality of the dam. Artificial neural networks (ANNs) are favored in hydrology due to their accuracy and robustness. This study modeled climatic effects on the water quality of Doğancı dam using a feed-forward neural network with one input, one hidden, and one output layer. Three models were tested using various

Стилі APA, Harvard, Vancouver, ISO та ін.

31

Xu, Changchun, Ting Li, Xujia Li, and Guangqing Yang. "Compaction Uniformity Evaluation of Subgrade in Highway Based on Principal Components Analysis and Back Propagation Neural Networks." Sustainability 15, no. 2 (2023): 1067. http://dx.doi.org/10.3390/su15021067.

Повний текст джерела

Анотація:

This paper proposes a comprehensive method for the compaction uniformity evaluation of subgrade in highways based on the principle components analysis and BP neural network. A field test on resilient and Young’s moduli of subgrade during compaction is performed on Zun-Qin highway. The moduli representing the compaction uniformity are the key factors in the principal component analysis, and the components are used as input in Back Propagation (BP) neural networks. The degree of variation and synthesis score of moduli in three subgrade sections are discussed, and the results show that the compre

Стилі APA, Harvard, Vancouver, ISO та ін.

32

Park, Ju-Yong, and Beom-Su Seo. "A study on resilient back-propagation neural network model for estimation of welding properties of flux cored wire." Journal of the Korean Society of Marine Engineering 42, no. 7 (2018): 531–38. http://dx.doi.org/10.5916/jkosme.2018.42.7.531.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

33

Chandra, Iwan. "Penentuan Lokasi Wireless Device Berbasis 3d Access Point Location Based." Journal of Intelligent System and Computation 4, no. 1 (2022): 01–06. http://dx.doi.org/10.52985/insyst.v4i1.215.

Повний текст джерела

Анотація:

Perkembangan penggunaan wireless saat ini telah mengubah cara hidup manusia. Dengan menganalisa gelombang yang diterima dari pemancar menuju sebuah perangkat tersebut. Untuk itu dibutuhkan sebuah model yang mampu memprediksi lokasi dari sebuah perangkat penerima. Pada penelitian ini, dikembangkan suatu metode untuk penentuan lokasi terhadap sebuah perangkat di dalam ruangan. Penelitian ini menerapkan konsep neural network dengan mendeteksi sinyal wireless yang ada di sekitar perangkat penerima. Sinyal-sinyal tersebut kemudian dikirimkan menuju server untuk kemudian diproses lebih lanjut. Prose

Стилі APA, Harvard, Vancouver, ISO та ін.

34

Huang, Lvwen, Lianliang Chen, Qin Wang, Siwen Yan, Xunbing Gao, and Jiangjiang Luan. "Regional Short-term Micro-climate Air Temperature Prediction with CBPNN." E3S Web of Conferences 53 (2018): 03009. http://dx.doi.org/10.1051/e3sconf/20185303009.

Повний текст джерела

Анотація:

This paper proposes a novel short-term air temperature prediction with three-layer Back Propagation Neural Network (BPNN) for the regional application of next 1-12 hours. With the continuous collection of eight real-time micro-climate parameters in the experimentation and demonstration stations in our university, the Multiple Stepwise Regression (MSR) is employed to screen the original historical data to find the parameter factors with greater contribution rate. On the basis of the Root Mean Square Error (RMSE) value evaluating the optimal fitting degree of the stepwise regression, the Levenbe

Стилі APA, Harvard, Vancouver, ISO та ін.

35

Mandal, Sumantra, P. V. Sivaprasad, and S. Venugopal. "Capability of a Feed-Forward Artificial Neural Network to Predict the Constitutive Flow Behavior of As Cast 304 Stainless Steel Under Hot Deformation." Journal of Engineering Materials and Technology 129, no. 2 (2006): 242–47. http://dx.doi.org/10.1115/1.2400276.

Повний текст джерела

Анотація:

A model is developed to predict the constitutive flow behavior of as cast 304 stainless steel during hot deformation using artificial neural network (ANN). The inputs of the neural network are strain, strain rate, and temperature, whereas flow stress is the output. Experimental data obtained from hot compression tests in the temperature range 1023-1523K, strain range 0.1-0.5, and strain rate range 10−3-102s−1 are employed to develop the model. A three-layer feed-forward ANN is trained with standard back propagation and some upgraded algorithms like resilient propagation (Rprop) and superSAB. T

Стилі APA, Harvard, Vancouver, ISO та ін.

36

Babbar, Sana Mohsin, and Tameer Hussain Langah. "Wind Power Prediction Using Neural Networks with Different Training Models." Indonesian Journal of Innovation and Applied Sciences (IJIAS) 2, no. 1 (2022): 12–17. http://dx.doi.org/10.47540/ijias.v2i1.340.

Повний текст джерела

Анотація:

Energy in any form is a vital source of producing electricity for daily utilization. Wind energy source as renewable energy is playing a pivotal role in generating power from electric gird owing to environmentally friendly feature. Due to the volatile and intermittent nature of wind energy, fluctuations and disparities occur in installing, monitoring, and planning in an energy management system. Therefore, forecasting and prediction are promising solutions to address mismanagement at the grid. Consequently, machine learning tools specifically neural networks have created a huge impact in forec

Стилі APA, Harvard, Vancouver, ISO та ін.

37

Güntürkün, Rüştü. "Determining the Amount of Anesthetic Medicine to Be Applied by Using Elman’s Recurrent Neural Networks via Resilient Back Propagation." Journal of Medical Systems 34, no. 4 (2009): 493–97. http://dx.doi.org/10.1007/s10916-009-9262-0.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

38

D, Hari Krishna, Anand M, Saravanan D, and Pushpalatha K. "Optimization of Machine Learning and Deep Learning Algorithms for Diagnosis of Cancer." ECS Transactions 107, no. 1 (2022): 9389–401. http://dx.doi.org/10.1149/10701.9389ecst.

Повний текст джерела

Анотація:

Machine learning and artificial intelligence has recently become a prominent technology. Given its popularity and strength in pattern recognition and categorization, many corporations and institutions have begun investing in healthcare research to improve illness prediction accuracy. Using these strategies, however, has several drawbacks. One of the primary issues is the lack of huge datasets for medical pictures. An introduction to deep learning in medical image processing from theoretical foundations to real-world applications. The article examines the general appeal of deep learning (DL), a

Стилі APA, Harvard, Vancouver, ISO та ін.

39

Zhu, Hongfei, Jorge Leandro, and Qing Lin. "Optimization of Artificial Neural Network (ANN) for Maximum Flood Inundation Forecasts." Water 13, no. 16 (2021): 2252. http://dx.doi.org/10.3390/w13162252.

Повний текст джерела

Анотація:

Flooding is the world’s most catastrophic natural event in terms of losses. The ability to forecast flood events is crucial for controlling the risk of flooding to society and the environment. Artificial neural networks (ANN) have been adopted in recent studies to provide fast flood inundation forecasts. In this paper, an existing ANN trained based on synthetic events was optimized in two directions: extending the training dataset with the use of hybrid dataset, and selection of the best training function based on six possible functions, namely conjugate gradient backpropagation with Fletcher–

Стилі APA, Harvard, Vancouver, ISO та ін.

40

Vinay, Kumar Jain. "A comparative analysis of neural network function: resilient back propagation algorithm (BPA) and radial basis functions (RBF) in multilingual environment." i-manager's Journal on Digital Signal Processing 10, no. 1 (2022): 9. http://dx.doi.org/10.26634/jdp.10.1.18639.

Повний текст джерела

Анотація:

The most convenient speech processing tool is Artificial Neural Networks (ANNs). The effectiveness has been tested with various real-time applications. The classifier using artificial neural networks identifies utterances based on features extracted from the speech signal. The proposed approach to multilingual speaker identification consists of two parts, such as a training part and a testing part. In the training part, the classifier is trained using speech feature vectors. The spoken language contains complete information, such as details about the content of the message and details about th

Стилі APA, Harvard, Vancouver, ISO та ін.

41

EL-Barry, A. M. A., and D. M. Habashy. "Simulation and prediction of optical characterization of casting (Adamantan-Fulgide) thin films using (CTANNs) approach." International Journal of Modern Physics B 33, no. 11 (2019): 1950093. http://dx.doi.org/10.1142/s0217979219500930.

Повний текст джерела

Анотація:

For reinforcement, the photochromic field and the cooperation between the theoretical and experimental branches of physics, the computational, theoretical artificial neural networks (CTANNs) and the resilient back propagation (R[Formula: see text]) training algorithm were used to model optical characterizations of casting (Admantan-Fulgide) thin films with different concentrations. The simulated values of ANN are in good agreement with the experimental data. The model was also used to predict values, which were not included in the training. The high precision of the model has been constructed.

Стилі APA, Harvard, Vancouver, ISO та ін.

42

Değişli, Beyza, and Vahdettin Demir. "Forecasting of Beyşehir Lake Levels Using Various Artificial Neural Network Training Algorithms." Knowledge-Based Engineering and Sciences 6, no. 1 (2025): 20–37. https://doi.org/10.51526/kbes.2025.6.1.20-37.

Повний текст джерела

Анотація:

Understanding and predicting lake water levels is crucial for sustainable water resource management, as fluctuations can significantly impact agriculture, energy production, and ecosystems, especially in the face of climate change and increasing water demand. Changes in lake levels also affect economic activities such as agriculture, energy production, and fishing. In this study, monthly average lake water levels (m) of Lake Beyehir, the largest freshwater source in Turkey, were predicted using different artificial neural network (ANN) training algorithms. The lake level data, obtained from th

Стилі APA, Harvard, Vancouver, ISO та ін.

43

Pani, Ajaya Kumar, and Hare Krishna Mohanta. "Online monitoring and control of particle size in the grinding process using least square support vector regression and resilient back propagation neural network." ISA Transactions 56 (May 2015): 206–21. http://dx.doi.org/10.1016/j.isatra.2014.11.011.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

44

R. A. Mohamed, Mahmoud. Y. El-Bakry, D. M. Habashy, and E. H. Aamer. "Mathematical Modeling of Photovoltaic Properties of Nipc/P-Si (Organic/Inorganic) Heterojunction by Using Artificial Neural Networks Model." JOURNAL OF ADVANCES IN PHYSICS 17 (June 3, 2020): 306–21. http://dx.doi.org/10.24297/jap.v17i.8718.

Повний текст джерела

Анотація:

In this research, the artificial neural network (ANN) and resilient back propagation (R-prop) training algorithm are utilized to model the photovoltaic properties of Nickel–phthalocyanine (NiPc/p-Si) heterojunction. The experimental data are extracted from experimental studies. Experimental data are utilized as inputs in the ANN model. Training of different structures of the ANN is processed to approach the minimum value of error. Eight artificial neural networks are trained to get a better mean square error (MSE) and best execution for the networks. The ANN performances are also investigated

Стилі APA, Harvard, Vancouver, ISO та ін.

45

Shalaby, Ahmed, and Alan Reggin. "Optimization of data collection needs for manual and automated network-level pavement condition ratings based on transverse variability and neural networks." Canadian Journal of Civil Engineering 34, no. 2 (2007): 139–46. http://dx.doi.org/10.1139/l06-126.

Повний текст джерела

Анотація:

The paper deals with two approaches to optimizing pavement condition surveys for the urban pavement network of the City of Winnipeg, Manitoba. First, a nonparametric statistical test was applied to assess the transverse variability of the data. The test compared the ratings for one lane with those of all lanes of each segment. The test concluded that the medians of the two groups are equal at a 92% confidence interval and that there are observed biases in the data. The bias can be eliminated if the surveyed lane is selected randomly. The second approach was to predict visual survey scores from

Стилі APA, Harvard, Vancouver, ISO та ін.

46

Al-Sharaeh, Saleh, Nancy Shaar, and Lara Shboul. "A Classical Machine Learning Model Scheduling in Industrial Wireless Sensor Networks." Journal of Hunan University Natural Sciences 49, no. 1 (2022): 24–30. http://dx.doi.org/10.55463/issn.1674-2974.49.1.4.

Повний текст джерела

Анотація:

Time synchronization is a primary issue in industrial wireless sensor networks (IWSNs). It helps to optimize the connection and preserve battery consumption, and thus increase the network lifetime. This study aims to identify the most effective factors that decrease the battery consumption and monitor the critical targets in wireless sensor networks (WSNs) through addressing the coverage and connectivity aware scheduling of sensor nodes (SNs). On the other hand, this paper aims to get a scheduling algorithm for industrial wireless sensor networks of SNs by using classical machine learning in t

Стилі APA, Harvard, Vancouver, ISO та ін.

47

Du, Yi-Chun, and Alphin Stephanus. "Levenberg-Marquardt Neural Network Algorithm for Degree of Arteriovenous Fistula Stenosis Classification Using a Dual Optical Photoplethysmography Sensor." Sensors 18, no. 7 (2018): 2322. http://dx.doi.org/10.3390/s18072322.

Повний текст джерела

Анотація:

This paper proposes a noninvasive dual optical photoplethysmography (PPG) sensor to classify the degree of arteriovenous fistula (AVF) stenosis in hemodialysis (HD) patients. Dual PPG measurement node (DPMN) becomes the primary tool in this work for detecting abnormal narrowing vessel simultaneously in multi-beds monitoring patients. The mean and variance of Rising Slope (RS) and Falling Slope (FS) values between before and after HD treatment was used as the major features to classify AVF stenosis. Multilayer perceptron neural networks (MLPN) training algorithms are implemented for this analys

Стилі APA, Harvard, Vancouver, ISO та ін.

48

Kottalanka, Srikanth, and Arivazhagan D. "An Efficient Patient Inflow Prediction Model For hospital Resource Management." Indonesian Journal of Electrical Engineering and Computer Science 5, no. 1 (2017): 809–17. https://doi.org/10.11591/ijeecs.v7.i3.pp809-817.

Повний текст джерела

Анотація:

There has been increasing demand in improving service provisioning in hospital resources management. Hospital industries work with strict budget constraint at the same time assures quality care. To achieve quality care with budget constraint an efficient prediction model is required. Recently there has been various time series based prediction model has been proposed to manage hospital resources such ambulance monitoring, emergency care and so on. These models are not efficient as they do not consider the nature of scenario such climate condition etc. To address this artificial intelligence is

Стилі APA, Harvard, Vancouver, ISO та ін.

49

Jallal, Mohammed Ali, Abdessalam El Yassini, Samira Chabaa, Abdelouhab Zeroual, and Saida Ibnyaich. "Multi-Target Learning Algorithm for Solar Radiation Components Forecasting Based on the Desired Tilt Angle of a Solar Energy System." Instrumentation Mesure Métrologie 20, no. 4 (2021): 187–93. http://dx.doi.org/10.18280/i2m.200402.

Повний текст джерела

Анотація:

Solar radiation components (SRC) forecasting with different tilt angles plays a key role for planning, managing, and controlling the solar energy system production. To overcome the gaps related to the intermittence and to the absence of SRC data, an accurate predictive model needs to be established. The main goal of the present work is to develop for solar system engineers and grid operators a precise predictive approach based on multi-target learning algorithm to forecast the hourly SRC measurements that is related to the city of Marrakesh (latitude 31°37′N, longitude 08°01′W, elevation 466m)

Стилі APA, Harvard, Vancouver, ISO та ін.

50

Al-batah, Mohammad Subhi, Mutasem Sh Alkhasawneh, Lea Tien Tay, Umi Kalthum Ngah, Habibah Hj Lateh, and Nor Ashidi Mat Isa. "Landslide Occurrence Prediction Using Trainable Cascade Forward Network and Multilayer Perceptron." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/512158.

Повний текст джерела

Анотація:

Landslides are one of the dangerous natural phenomena that hinder the development in Penang Island, Malaysia. Therefore, finding the reliable method to predict the occurrence of landslides is still the research of interest. In this paper, two models of artificial neural network, namely, Multilayer Perceptron (MLP) and Cascade Forward Neural Network (CFNN), are introduced to predict the landslide hazard map of Penang Island. These two models were tested and compared using eleven machine learning algorithms, that is, Levenberg Marquardt, Broyden Fletcher Goldfarb, Resilient Back Propagation, Sca

Стилі APA, Harvard, Vancouver, ISO та ін.

Ми пропонуємо знижки на всі преміум-плани для авторів, чиї праці увійшли до тематичних добірок літератури. Зв'яжіться з нами, щоб отримати унікальний промокод!