Neural Network Toolbox

Neural Network Toolbox. Neural Network Toolbox™ provides functions and apps for modeling complex nonlinear systems that are not easily modeled with a closed-form equation. Neural Network Toolbox supports supervised learning with feedforward, radial basis, and dynamic networks. It also supports unsupervised learning with self-organizing maps and competitive layers. With the toolbox you can design, train, visualize, and simulate neural networks. You can use Neural Network Toolbox for applications such as data fitting, pattern recognition, clustering, time-series prediction, and dynamic system modeling and control. To speed up training and handle large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox™.

References in zbMATH (referenced in 166 articles )

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  1. Xu, Wanting; Stein, Michael L.; Wisher, Ian: Modeling and predicting chaotic circuit data (2019)
  2. Jafrasteh, Bahram; Fathianpour, Nader; Suárez, Alberto: Comparison of machine learning methods for copper ore grade estimation (2018)
  3. Sameh, Adaily; Abdelkader, Mbarek; Tarek, Garna; José, Ragot: Optimal multimodel representation by Laguerre filters applied to a communicating two tank system (2018)
  4. Campisi, Laura Donatella: Sketching the temperature history of geological samples: analyses of diffusion profiles using multilayer perceptrons (2017)
  5. Francesco Giannini, Vincenzo Laveglia, Alessandro Rossi, Dario Zanca, Andrea Zugarini: Neural Networks for Beginners. A fast implementation in Matlab, Torch, TensorFlow (2017) arXiv
  6. Vapnik, Vladimir; Izmailov, Rauf: Knowledge transfer in SVM and neural networks (2017)
  7. Wang, Li; Lin, Shimin; Yang, Jingfeng; Zhang, Nanfeng; Yang, Ji; Li, Yong; Zhou, Handong; Yang, Feng; Li, Zhifu: Dynamic traffic congestion simulation and dissipation control based on traffic flow theory model and neural network data calibration algorithm (2017)
  8. Xue, Dingyü; Chen, YangQuan: Scientific computing with MATLAB (2016)
  9. Zhang, Yu’nong; Xiao, Zhengli; Ding, Sitong; Mao, Mingzhi; Liu, Jinrong: WASD neural network activated by bipolar sigmoid functions together with subsequent iterations (2016)
  10. Aghajani, Hamed Farshbaf; Salehzadeh, Hossein; Shahnazari, Habib: Stability analysis of sandy slope considering anisotropy effect in friction angle (2015) ioport
  11. Balachandar, C.; Arunkumar, S.; Venkatesan, M.: Computational heat transfer analysis and combined ANN-GA optimization of hollow cylindrical pin fin on a vertical base plate (2015) ioport
  12. Campisi, Laura D.: Multilayer perceptrons as function approximators for analytical solutions of the diffusion equation (2015)
  13. Kalyan Veeramachaneni; Ignacio Arnaldo; Owen Derby; Una-May O’Reilly: FlexGP (2015) not zbMATH
  14. Khouaja, Anis; Garna, Tarek; Ragot, José; Messaoud, Hassani: Nonlinear predictive controller based on S-PARAFAC Volterra models applied to a communicating two-tank system (2015)
  15. Mahmoud, Magdi S.; Hussain, S. Azher: Adaptive PI secondary control for smart autonomous microgrid systems (2015)
  16. Moussa, H.; Benallal, M. A.; Goyet, C.; Lefèvre, N.; El Jai, M. C.; Guglielmi, V.; Touratier, F.: A comparison of multiple non-linear regression and neural network techniques for sea surface salinity estimation in the tropical Atlantic Ocean based on satellite data (2015)
  17. Stamenkovic, Dragan D.; Popovic, Vladimir M.: Warranty optimisation based on the prediction of costs to the manufacturer using neural network model and Monte Carlo simulation (2015)
  18. Worapradya, Kiatkajohn; Thanakijkasem, Purit: Proactive scheduling for steelmaking-continuous casting plant with uncertain machine breakdown using distribution-based robustness and decomposed artificial neural network (2015)
  19. Cecchini, Giulio; Lozito, Gabriele Maria; Schmid, Maurizio; Conforto, Silvia; Fulginei, Francesco Riganti; Bibbo, Daniele: Neural networks for muscle forces prediction in cycling (2014)
  20. Fodor, János (ed.); Fullér, Robert (ed.): Advances in soft computing, intelligent robotics and control (2014)

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