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. Wang, Jun; Pan, Huopo; Liu, Fajiang: Forecasting crude oil price and stock price by jump stochastic time effective neural network model (2012)
  2. Demetgul, M.; Unal, M.; Tansel, I. N.; Yazıcıoğlu, O.: Fault diagnosis on bottle filling plant using genetic-based neural network (2011) ioport
  3. Kamimura, Ryotaro: Constrained information maximization by free energy minimization (2011)
  4. Kerh, Tienfuan; Huang, Chuhsiung; Gunaratnam, David: Neural network approach for analyzing seismic data to identify potentially hazardous bridges (2011) ioport
  5. Komendantskaya, Ekaterina: Unification neural networks: unification by error-correction learning (2011)
  6. Mantzaris, Dimitrios; Anastassopoulos, George; Adamopoulos, Adam: Genetic algorithm pruning of probabilistic neural networks in medical disease estimation (2011) ioport
  7. Mehrabian, Ali Reza; Yousefi-Koma, Aghil: A novel technique for optimal placement of piezoelectric actuators on smart structures (2011)
  8. Merad, L.; Bendimerad, F. T.; Meriah, Sidi Mohammed: Design and resonant frequency calculation of rectangular microstrip antennas (2011)
  9. Mohammadzaheri, Morteza; Chen, Lei: Intelligent control of a nonlinear tank reactor (2011)
  10. Mubiru, James: Using artificial neural networks to predict direct solar irradiation (2011) ioport
  11. Rahmani, Hossein; Bonyadi, Mohammad Reza; Momeni, Amir; Moghaddam, Mohsen Ebrahimi; Abbaspour, Maghsoud: Hardware design of a new genetic based disk scheduling method (2011)
  12. 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
  13. Song, Hong; Fraanje, Rufus; Schitter, Georg; Vdovin, Gleb; Verhaegen, Michel: Controller design for a high-sampling-rate closed-loop adaptive optics system with piezo-driven deformable mirror (2011)
  14. Yu, Xinghuo; Wang, Bin; Batbayar, Batsukh; Wang, Liuping; Man, Zhihong: An improved training algorithm for feedforward neural network learning based on terminal attractors (2011)
  15. Arslan, M. Hakan: Predicting of torsional strength of RC beams by using different artificial neural network algorithms and building codes (2010)
  16. Cetişli, Bayram; Barkana, Atalay: Speeding up the scaled conjugate gradient algorithm and its application in neuro-fuzzy classifier training (2010)
  17. Chapotot, Florian; Becq, Guillaume: Automated sleep-wake staging combining robust feature extraction, artificial neural network classification, and flexible decision rules (2010)
  18. El-Shafie, Ahmed; Abdelazim, T.; Noureldin, A.: Neural network modeling of time-dependent creep deformations in masonry structures (2010) ioport
  19. Farias, G.; Santos, M.; López, V.: Making decisions on brain tumor diagnosis by soft computing techniques (2010) ioport
  20. Grage, Halfdan; Holst, Jan; Lindgren, Georg; Saklak, Mietek: Level crossing prediction with neural networks (2010)

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