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 178 articles )

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  1. 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
  2. Campisi, Laura D.: Multilayer perceptrons as function approximators for analytical solutions of the diffusion equation (2015)
  3. Kalyan Veeramachaneni; Ignacio Arnaldo; Owen Derby; Una-May O’Reilly: FlexGP (2015) not zbMATH
  4. 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)
  5. Mahmoud, Magdi S.; Hussain, S. Azher: Adaptive PI secondary control for smart autonomous microgrid systems (2015)
  6. 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)
  7. 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)
  8. 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)
  9. Fodor, János (ed.); Fullér, Robert (ed.): Advances in soft computing, intelligent robotics and control (2014)
  10. Niu, Hongli; Wang, Jun: Financial time series prediction by a random data-time effective RBF neural network (2014) ioport
  11. Patel, Maulika S.; Mazumdar, Himanshu S.: Knowledge base and neural network approach for protein secondary structure prediction (2014)
  12. Sarah, Gnaba; Garna, Tarek; Bouzrara, Kais; Ragot, José; Messaoud, Hassani: Online identification of the bilinear model expansion on Laguerre orthonormal bases (2014)
  13. Mo, Haiyan; Wang, Jun: Volatility degree forecasting of stock market by stochastic time strength neural network (2013)
  14. Ngaopitakkul, A.; Bunjongjit, S.: An application of a discrete wavelet transform and a back-propagation neural network algorithm for fault diagnosis on single-circuit transmission line (2013)
  15. Ranaee, Vahid; Ebrahimzadeh, Ata: Control chart pattern recognition using neural networks and efficient features: a comparative study (2013) ioport
  16. Yang, Keng-Chieh; Yang, Conna; Chao, Pei-Yao; Shih, Po-Hong: Applying artificial neural network to predict semiconductor machine outliers (2013) ioport
  17. Bojórquez, Edén; Bojórquez, Juan; Ruiz, Sonia E.; Reyes-Salazar, Alfredo: Prediction of inelastic response spectra using artificial neural networks (2012)
  18. Cho, Soo-Yong; Ahn, Kook-Young; Lee, Young-Duk; Kim, Young-Cheol: Optimal design of a centrifugal compressor impeller using evolutionary algorithms (2012) ioport
  19. Mohammadzaheri, Morteza; Chen, Lei; Grainger, Steven: A critical review of the most popular types of neuro control (2012)
  20. Montaseri, Ghazal; Yazdanpanah, Mohammad Javad: Predictive control of uncertain nonlinear parabolic PDE systems using a Galerkin/neural-network-based model (2012)

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