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 127 articles )

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  1. Francesco Giannini, Vincenzo Laveglia, Alessandro Rossi, Dario Zanca, Andrea Zugarini: Neural Networks for Beginners. A fast implementation in Matlab, Torch, TensorFlow (2017) arXiv
  2. Xue, Dingyü; Chen, YangQuan: Scientific computing with MATLAB (2016)
  3. Aghajani, Hamed Farshbaf; Salehzadeh, Hossein; Shahnazari, Habib: Stability analysis of sandy slope considering anisotropy effect in friction angle (2015) ioport
  4. 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
  5. 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)
  6. Niu, Hongli; Wang, Jun: Financial time series prediction by a random data-time effective RBF neural network (2014) ioport
  7. Patel, Maulika S.; Mazumdar, Himanshu S.: Knowledge base and neural network approach for protein secondary structure prediction (2014)
  8. Mo, Haiyan; Wang, Jun: Volatility degree forecasting of stock market by stochastic time strength neural network (2013)
  9. Yang, Keng-Chieh; Yang, Conna; Chao, Pei-Yao; Shih, Po-Hong: Applying artificial neural network to predict semiconductor machine outliers (2013) ioport
  10. Bojórquez, Edén; Bojórquez, Juan; Ruiz, Sonia E.; Reyes-Salazar, Alfredo: Prediction of inelastic response spectra using artificial neural networks (2012)
  11. Cho, Soo-Yong; Ahn, Kook-Young; Lee, Young-Duk; Kim, Young-Cheol: Optimal design of a centrifugal compressor impeller using evolutionary algorithms (2012) ioport
  12. Montaseri, Ghazal; Yazdanpanah, Mohammad Javad: Predictive control of uncertain nonlinear parabolic PDE systems using a Galerkin/neural-network-based model (2012)
  13. Wang, Jun; Pan, Huopo; Liu, Fajiang: Forecasting crude oil price and stock price by jump stochastic time effective neural network model (2012)
  14. Demetgul, M.; Unal, M.; Tansel, I.N.; Yazıcıoğlu, O.: Fault diagnosis on bottle filling plant using genetic-based neural network (2011) ioport
  15. Kerh, Tienfuan; Huang, Chuhsiung; Gunaratnam, David: Neural network approach for analyzing seismic data to identify potentially hazardous bridges (2011) ioport
  16. Komendantskaya, Ekaterina: Unification neural networks: unification by error-correction learning (2011)
  17. Mehrabian, Ali Reza; Yousefi-Koma, Aghil: A novel technique for optimal placement of piezoelectric actuators on smart structures (2011)
  18. Mubiru, James: Using artificial neural networks to predict direct solar irradiation (2011) ioport
  19. Rahmani, Hossein; Bonyadi, Mohammad Reza; Momeni, Amir; Moghaddam, Mohsen Ebrahimi; Abbaspour, Maghsoud: Hardware design of a new genetic based disk scheduling method (2011)
  20. Silva-Ramírez, Esther-Lydia; Pino-Mejías, Rafael; López-Coello, Manuel; Cubiles-de-la-Vega, María-Dolores: Missing value imputation on missing completely at random data using multilayer perceptrons (2011) ioport

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