Relevant bibliographies by topics / SVR Neural Network

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'SVR Neural Network'

Author: Grafiati
Published: 5 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "SVR Neural Network"

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Jayadianti, Herlina, Tedy Agung Cahyadi, Nur Ali Amri, and Muhammad Fathurrahman Pitayandanu. "METODE KOMPARASI ARTIFICIAL NEURAL NETWORK PADA PREDIKSI CURAH HUJAN - LITERATURE REVIEW." Jurnal Tekno Insentif 14, no. 2 (2020): 48–53. http://dx.doi.org/10.36787/jti.v14i2.150.

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Abstrak - Penelitian untuk mencari model prediksi curah hujan yang akurat di berbagai bidang sudah banyak dilakukan, maka dilakukan di-review kembali guna membantu proses penyaliran dalam perusahaan tambang. Review dilakukan dengan membandingkan hasil dari setiap model yang telah dilakukan pada beberapa penelitian sebelumnya. Penelitian ini menggunakan metode kuantitatif. Model yang dibandingkan pada penelitian di antaranya yaitu model Fuzzy, Fast Fourier Transformation (FFT), Emotional Artificial Neural Network (EANN), Artificial Neural Network (ANN), Adaptive Ensemble Empirical Mode Decompos
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Guo, Kong Hui, and Xian Yun Wang. "Comparisons of Support Vector Regression and Neural Network in Modelling the Hydraulic Damper." Advanced Materials Research 403-408 (November 2011): 3805–12. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3805.

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Nonparametric models of hydraulic damper based on support vector regression (SVR) are developed. Then these models are compared with two kinds neural network models. One is backpropagation neural network (BPNN) model; another is radial basis function neural network (RBFNN) model. Comparisons are carried out both on virtual damper and actual damper. The force-velocity relation of a virtual damper is obtained based on a rheological model. Then these data are used to identify the characteristics of the virtual damper. The dynamometer measurements of an actual displacement-dependent damper are obt
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Edy, Fradinata, Suthummanon Sakesun, and Suntiamorntut Wannarat. "Initial Optimal Parameters of Artificial Neural Network and Support Vector Regression." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (2018): 3341–48. https://doi.org/10.11591/ijece.v8i5.pp3341-3348.

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This paper presents architecture of backpropagation Artificial Neural Network (ANN) and Support Vector Regression (SVR) models in supervised learning process for cement demand dataset. This study aims to identify the effectiveness of each parameter of mean square error (MSE) indicators for time series dataset. The study varies different random sample in each demand parameter in the network of ANN and support vector function as well. The variations of percent datasets from activation function, learning rate of sigmoid and purelin, hidden layer, neurons, and training function should be applied f
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Lin, Kuo Ping. "Application of Least-Squares Support Vector Regression with PSO for CPU Performance Forecasting." Advanced Materials Research 630 (December 2012): 366–71. http://dx.doi.org/10.4028/www.scientific.net/amr.630.366.

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The success of CPU performance prediction will make many benefits. This study adopts the least-squares support vector regression (LS-SVR) with particle swarm optimization (PSO) algorithm to improver accuracy of CPU performance prediction. LS-SVR with PSO, support vector regression (SVR) with PSO, general regression neural network (GRNN), radial basis neural network (RBNN), and linear regression are employed for CPU performance prediction. Empirical results indicate that the LS-SVR (Linear kernel) with PSO has better performance in terms of forecasting accuracy than the other methods. Therefore
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Liu, Jiao, Guoyou Shi, and Kaige Zhu. "Vessel Trajectory Prediction Model Based on AIS Sensor Data and Adaptive Chaos Differential Evolution Support Vector Regression (ACDE-SVR)." Applied Sciences 9, no. 15 (2019): 2983. http://dx.doi.org/10.3390/app9152983.

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There are difficulties in obtaining accurate modeling of ship trajectories with traditional prediction methods. For example, neural networks are prone to falling into local optima and there are a small number of Automatic Identification System (AIS) information samples regarding target ships acquired in real time at sea. In order to improve the accuracy of ship trajectory predictions and solve these problems, a trajectory prediction model based on support vector regression (SVR) is proposed. Ship speed, course, time stamp, longitude and latitude from AIS data were selected as sample features a
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Jiang, Lei, Da Teng, and Yue Zhao. "A Soft Measurement Method for the Tail Diameter in the Growing Process of Czochralski Silicon Single Crystals." Applied Sciences 14, no. 4 (2024): 1569. http://dx.doi.org/10.3390/app14041569.

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In the Czochralski silicon single crystal growth process, the tail diameter is a key parameter that cannot be directly measured. In this paper, we propose a real-time soft measurement method that combines a deep belief network (DBN) and a support vector regression (SVR) network based on system identification to accurately predict the crystal diameter. The main steps of the proposed method are as follows: First, we address the delay problem of the effects of the temperature and crystal pulling speed on the tail diameter growth by using a back propagation (BP) neural network based on the mean im
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Fradinata, Edy, Sakesun Suthummanon, and Wannarat Suntiamorntut. "Initial Optimal Parameters of Artificial Neural Network and Support Vector Regression." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (2018): 3341. http://dx.doi.org/10.11591/ijece.v8i5.pp3341-3348.

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This paper presents architecture of backpropagation Artificial Neural Network (ANN) and Support Vector Regression (SVR) models in supervised learning process for cement demand dataset. This study aims to identify the effectiveness of each parameter of mean square error (MSE) indicators for time series dataset. The study varies different random sample in each demand parameter in the network of ANN and support vector function as well. The variations of percent datasets from activation function, learning rate of sigmoid and purelin, hidden layer, neurons, and training function should be applied f
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Pyo, JongCheol, Hongtao Duan, Mayzonee Ligaray, et al. "An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery." Remote Sensing 12, no. 7 (2020): 1073. http://dx.doi.org/10.3390/rs12071073.

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Hyperspectral image sensing can be used to effectively detect the distribution of harmful cyanobacteria. To accomplish this, physical- and/or model-based simulations have been conducted to perform an atmospheric correction (AC) and an estimation of pigments, including phycocyanin (PC) and chlorophyll-a (Chl-a), in cyanobacteria. However, such simulations were undesirable in certain cases, due to the difficulty of representing dynamically changing aerosol and water vapor in the atmosphere and the optical complexity of inland water. Thus, this study was focused on the development of a deep neura
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Fu, Jiake, Huijing Tian, Lingguang Song, Mingchao Li, Shuo Bai, and Qiubing Ren. "Productivity estimation of cutter suction dredger operation through data mining and learning from real-time big data." Engineering, Construction and Architectural Management 28, no. 7 (2021): 2023–41. http://dx.doi.org/10.1108/ecam-05-2020-0357.

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PurposeThis paper presents a new approach of productivity estimation of cutter suction dredger operation through data mining and learning from real-time big data.Design/methodology/approachThe paper used big data, data mining and machine learning techniques to extract features of cutter suction dredgers (CSD) for predicting its productivity. ElasticNet-SVR (Elastic Net-Support Vector Machine) method is used to filter the original monitoring data. Along with the actual working conditions of CSD, 15 features were selected. Then, a box plot was used to clean the corresponding data by filtering ou
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Raheja, Supriya, and Sahil Malik. "Prediction of Air Quality Using LSTM Recurrent Neural Network." International Journal of Software Innovation 10, no. 1 (2022): 1–16. http://dx.doi.org/10.4018/ijsi.297982.

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Rapid increase of Industrialization and Urbanization significantly draws the interest of researchers towards the prediction of air quality. Efficient modelling of air quality parameters using deep learning methods can facilitate the imminent implications of air pollution. However, existing methods weakens at consideration of long-term dependencies for multiple parameters. The present study aims prediction of air quality of New Delhi based on concentration of multiple parameters namely PM2.5, PM10, CO, O3, NO2 and SO2. The study uses long short-term memory (LSTM) approach due to its efficiency
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Dissertations / Theses on the topic "SVR Neural Network"

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Sa'ad, Aisha. "Developing integrated maintenance strategies for renewable energy sources based on analytical methods and artificial intelligence (AI) : comparisons and case study." Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0080.

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Au cours de ces récentes années, le développement des énergies renouvelables, en particulier l'énergie solaire et l'énergie éolienne, a attiré comme méthode alternative de production d'énergie, l'attention du monde entier avec une croissance exceptionnelle de sa production. Selon le rapport de Global Energy, l'énergie solaire mondiale devrait avoir atteint une capacité cumulée de 1 TW, tandis que l'énergie éolienne devrait avoir été multipliée par 3 ou 4 mégas par rapport à la production en 2020. Cette augmentation des énergies solaire et éolienne implique des investissements financiers très i
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Hamdi, Takoua. "Analyse de l'évolution de la glycémie des patients diabétiques insulinodépendants." Electronic Thesis or Diss., Toulon, 2019. http://www.theses.fr/2019TOUL0004.

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L'objectif principal de cette thèse est d'aider le diabète de type 1 (DTl) à contrôler et stabiliser son taux de glycémie. Pour cela, une analyse de l'évolution de la glycémie est nécessaire, ensuite et après l'enregistrement des valeurs de la glycémie à l'aide des CGM, une bonne méthode de prédiction de la glycémie est indispensable pour que le patient puisse ajuster la dose d'insuline injecté sur la base de ces valeurs prédites. Dans ce cadre, nous avons focalisé dans le premier chapitre une étude sur le principe de la régulation de la glycémie, dont nous avons présenté l'homéostasie glycémi
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Sazli, Murat Husnu Işık Can. "Neural network applications to turbo decoding." Related Electronic Resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2003. http://wwwlib.umi.com/cr/syr/main.

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Demus, Justin Cole. "Prognostic Health Management Systems for More Electric Aircraft Applications." Miami University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=miami1631047006902809.

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Morphet, Steven Brian Işık Can. "Modeling neural networks via linguistically interpretable fuzzy inference systems." Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2004. http://wwwlib.umi.com/cr/syr/main.

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Kang, Sanggil. "Identification of neural network input types and input ranking via sensitivity analysis." Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2002. http://wwwlib.umi.com/cr/syr/main.

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Jansson, Daniel, and Rasmus Blomstrand. "REAL-TIME PREDICTION OF SHIMS DIMENSIONS IN POWER TRANSFER UNITS USING MACHINE LEARNING." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-45615.

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Waseem, Athar, and A. H. M. Sadath Hossain. "MIMO Channel Equalization and Symbol Detection using Multilayer Neural Network." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2345.

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In recent years Multiple Input Multiple Output (MIMO) systems have been employed in wireless communication systems to reach the goals of high data rate. A MIMO use multiple antennas at both transmitting and receiving ends. These antennas communicate with each other on the same frequency band and help in linearly increasing the channel capacity. Due to the multi paths wireless channels face the problem of channel fading which cause Inter Symbol Interference (ISI). Each channel path has an independent path delay, independent path loss or path gain and phase shift, cause deformations in a signal
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Borg, Denis. "Redes neurais e support vector machines como técnicas de diagnósticos em medições industriais de nível por tecnologia tipo radar sem contato e apoio à decisão para a melhoria de sua aplicação." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/18/18153/tde-17022017-105129/.

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O objetivo dessa tese é detectar e classificar problemas de medição de nível por princípio de radar de propagação de onda livre por meio de RNA (redes neurais artificiais) e SVM (support vector machines) aliados à tratamentos estatísticos. Um primeiro cenário com ambiente controlado foi montado para a obtenção de dados preliminares. Na sequência, outros três cenários empregaram dados industriais reais. Para tanto, algumas topologias de redes neurais em quatro cenários diferentes foram testadas e foi possível demonstrar o funcionamento eficiente da RNA com acertos de 100% para o primeiro cenári
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Massana, i. Raurich Joaquim. "Data-driven models for building energy efficiency monitoring." Doctoral thesis, Universitat de Girona, 2018. http://hdl.handle.net/10803/482148.

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Nowadays, energy is absolutely necessary all over the world. Taking into account the advantages that it presents in transport and the needs of homes and industry, energy is transformed into electricity. Bearing in mind the expansion of electricity, initiatives like Horizon 2020, pursue the objective of a more sustainable future: reducing the emissions of carbon and electricity consumption and increasing the use of renewable energies. As an answer to the shortcomings of the traditional electrical network, such as large distances to the point of consumption, low levels of flexibility, low sus
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Books on the topic "SVR Neural Network"

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Association canadienne-francaise pour l'avancement des sciences. Congrès. Les techniques d'intelligence artificielle appliquées aux technologies de l'information: Réflexions sur les approches neuroniques, symboliques et numériques appliquées à la vision, l'écrit, la parole et le biomédical : actes du Colloque multidisciplinaire L'intelligence artificielle dans les technologies de l'information tenu dans le cadre du Congrès de l'Acfas à Montréal en mai 1996. ACFAS, 1997.

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Gates, Bill. Business @ the Speed of Thought. Grand Central Publishing, 2009.

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Gates, Bill. Bill Gates: Troc đuo tư duy = Business @ the speed of thought : insight from the world's greatest I.T. entrepreneur. NXB Trke, 2002.

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Gates, Bill. Business @ the speed of thought: Using a digital nervous system. BCA, 1999.

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Gates, Bill. Business @ the speed of thought: Using a digital nervous system. Warner Books, 1999.

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Gates, Bill. Shu wei shen jing xi tong: Yü si kao deng kuai di ming ri shi jie. Shang yeh zhou kan chu ban gu fen yu xian gong si, 1999.

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Gates, Bill. Business @ the speed of thought: Succeeding in the digital economy. Penguin, 2000.

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Gates, Bill. Los negocios en la era digital. Plaza & Janes, 1999.

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Gates, Bill. Wei lai shi su: Shu zi shen jing xi tong yu shang wu xin si wei. Beijing da xue chu ban she, 1999.

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Gates, Bill. Business @ the speed of thought: Using a digital nervous system. Warner Books, 1999.

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Book chapters on the topic "SVR Neural Network"

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Wang, Lingzhi, and Jiansheng Wu. "Neural Network Ensemble Model Using PPR and LS-SVR for Stock Market Forecasting." In Advanced Intelligent Computing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24728-6_1.

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Gaur, Shishir, Anne Johannet, Didier Graillot, and Padam Jee Omar. "Modeling of Groundwater Level Using Artificial Neural Network Algorithm and WA-SVR Model." In Groundwater Resources Development and Planning in the Semi-Arid Region. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68124-1_7.

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Fang, Li, Yuan Liu, Qing Zhao, Jing Yang, and Luoyifan Zhong. "Public Budget Revenue Model Based on SVR and Neural Network-A Case Study of Shanxi Province." In Atlantis Highlights in Intelligent Systems. Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-200-2_91.

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Fu, Yonggang, Ruimin Shen, Hongtao Lu, and Xusheng Lei. "SVR-Based Oblivious Watermarking Scheme." In Advances in Neural Networks – ISNN 2005. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11427445_127.

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Ma, Ruipeng, Di Wu, Tao Hu, Dong Yi, Yuqiao Zhang, and Jianxia Chen. "Automatic Modulation Classification Based on One-Dimensional Convolution Feature Fusion Network." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_90.

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AbstractDeep learning method has been gradually applied to Automatic Modulation Classification (AMC) because of its excellent performance. In this paper, a lightweight one-dimensional convolutional neural network module (OnedimCNN) is proposed. We explore the recognition effects of this module and other different neural networks on IQ features and AP features. We conclude that the two features are complementary under high and low SNR. Therefore, we use this module and probabilistic principal component analysis (PPCA) to fuse the two features, and propose a one-dimensional convolution feature f
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Wang, Hui, Daoying Pi, Youxian Sun, Chi Xu, and Sizhen Chu. "Fast Online SVR Algorithm Based Adaptive Internal Model Control." In Advances in Neural Networks - ISNN 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11760023_136.

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Tamukoh, Hakaru, Keiichi Horio, and Takeshi Yamakawa. "A Digital Hardware Architecture of Self-Organizing Relationship (SOR) Network." In Neural Information Processing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11893295_129.

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Salmerón, Moisés, Julio Ortega, Carlos García Puntonet, Alberto Prieto, and Ignacio Rojas. "SSA, SVD, QR-cp, and RBF Model Reduction." In Artificial Neural Networks — ICANN 2002. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46084-5_96.

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Lu, Chi-Jie, Jui-Yu Wu, Chih-Chou Chiu, and Yi-Jun Tsai. "Predicting Stock Index Using an Integrated Model of NLICA, SVR and PSO." In Advances in Neural Networks – ISNN 2011. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21111-9_25.

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Xue, Yaofeng, and Jingqi Yuan. "A SVR-Based Multiple Modeling Algorithm for Antibiotic Fermentation Process Using FCM." In Advances in Neural Networks – ISNN 2005. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11427469_110.

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Conference papers on the topic "SVR Neural Network"

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Zhou, Liang, Yinjie Zhu, Yi Qian, et al. "Online SVR Neural Network Based Ultra High Voltage Transmission Early Warning of Construction Progress." In 2024 IEEE 8th Conference on Energy Internet and Energy System Integration (EI2). IEEE, 2024. https://doi.org/10.1109/ei264398.2024.10991899.

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Zhang, Biao, Songchang Jin, Qianying Ouyang, et al. "SVR-AVT: Scale Variation Robust Active Visual Tracking." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650947.

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Francavilla, Mauro, and Andrea Schiavoni. "Averaged SAR Computation: A Neural Network Approach." In 16th International Zurich Symposium and Technical Exposition on Electromagnetic Compatibility. IEEE, 2005. https://doi.org/10.23919/emc.2005.10806418.

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Herrera, J., J. Núñez-Kasaneva, N. Pereda, A. Alvarado, and G. Saavedra. "A Hybrid Convolutional Neural Network for Classification and SNR Estimation of QAM Modulation Formats." In Latin America Optics and Photonics Conference. Optica Publishing Group, 2024. https://doi.org/10.1364/laop.2024.tu5c.3.

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A hybrid convolutional neural network to identify modulation format and estimate signal-to-noise ratio (SNR) using received constellations is shown. Accuracy of 91.38% and average error of 0.83 [dB] are shown estimating modulation format and SNR.
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Feng, Zhichen, Yaqian Gao, Huang Ye, and Jian Zhang. "A Feature Extraction Framework for 3D Scientific Voxel Object Using SVD and Neural Network." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10649920.

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Karasuyama, M., D. Kitakoshi, and R. Nakano. "Revised Optimizer of SVR Hyperparameters Minimizing Cross-Validation Error." In The 2006 IEEE International Joint Conference on Neural Network Proceedings. IEEE, 2006. http://dx.doi.org/10.1109/ijcnn.2006.246698.

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Serafim, Paulo Bruno S., Yuri Lenon B. Nogueira, Creto A. Vidal, and Joaquim B. Cavalcante Neto. "Towards Playing a 3D First-Person Shooter Game Using a Classification Deep Neural Network Architecture." In 2017 19th Symposium on Virtual and Augmented Reality (SVR). IEEE, 2017. http://dx.doi.org/10.1109/svr.2017.24.

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de Sa Delgado Neto, Afonso, and Rafael Mendes Campello. "Chess Position Identification using Pieces Classification Based on Synthetic Images Generation and Deep Neural Network Fine-Tuning." In 2019 21st Symposium on Virtual and Augmented Reality (SVR). IEEE, 2019. http://dx.doi.org/10.1109/svr.2019.00038.

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Jayadeva, R. Khemchandani, and S. Chandra. "Regularized Least Squares Twin SVR for the Simultaneous Learning of a Function and its Derivative." In The 2006 IEEE International Joint Conference on Neural Network Proceedings. IEEE, 2006. http://dx.doi.org/10.1109/ijcnn.2006.246826.

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Lyu, Jiaqi, and Souran Manoochehri. "Dimensional Prediction for FDM Machines Using Artificial Neural Network and Support Vector Regression." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97963.

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Abstract With the development of Fused Deposition Modeling (FDM) technology, the quality of fabricated parts is getting more attention. The present study highlights the predictive model for dimensional accuracy in the FDM process. Three process parameters, namely extruder temperature, layer thickness, and infill density, are considered in the model. To achieve better prediction accuracy, three models are studied, namely multivariate linear regression, Artificial Neural Network (ANN), and Support Vector Regression (SVR). The models are used to characterize the complex relationship between the i
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Reports on the topic "SVR Neural Network"

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Naseem, Shahid. Hand written digits classification and recognition using convolutional neural networks by implementing the techniques of MLP and SVM. Peeref, 2023. http://dx.doi.org/10.54985/peeref.2303p8226220.

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Alwan, Iktimal, Dennis D. Spencer, and Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.

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Objective: To compare the performance of popular machine learning algorithms (ML) in mapping the sensorimotor cortex (SM) and identifying the anterior lip of the central sulcus (CS). Methods: We evaluated support vector machines (SVMs), random forest (RF), decision trees (DT), single layer perceptron (SLP), and multilayer perceptron (MLP) against standard logistic regression (LR) to identify the SM cortex employing validated features from six-minute of NREM sleep icEEG data and applying standard common hyperparameters and 10-fold cross-validation. Each algorithm was tested using vetted feature
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Hajj, Ramez, Marshall Thompson, Renan Santos Maia, et al. Updates to Mechanistic-Empirical Design Inputs for Illinois Flexible Pavements. Illinois Center for Transportation, 2024. http://dx.doi.org/10.36501/0197-9191/24-010.

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This study reviews the Illinois Department of Transportation’s full-depth asphalt, limiting strain criterion, and asphalt over rubblized concrete design procedures, considering technological advancements in hot-mix asphalt—namely, the increased use of recycled materials and modified asphalt binders. The researchers evaluated the current |E*| algorithm by conducting laboratory tests with four mix designs and seven asphalt binders of different Superpave performance grades. They compared predictive models, including the current Illinois modulus algorithm as well as the Witczak, Hirsch, and newly
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