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. Sharma, Adesh K.; Sharma, R. K.; Kasana, H. S.: Empirical comparisons of feed-forward connectionist and conventional regression models for prediction of first lactation 305-day milk yield in karan fries dairy cows (2006) ioport
  2. Thomas, Bertil; Soleimani-Mohseni, Mohsen: Artificial neural network models for indoor temperature prediction: investigations in two buildings (2006) ioport
  3. Trivailo, P. M.; Dulikravich, G. S.; Sgarioto, D.; Gilbert, T.: Inverse problem of aircraft structural parameter estimation: application of neural networks (2006)
  4. Lee, Jong Min; Lee, Jay H.: Approximate dynamic programming-based approaches for input--output data-driven control of nonlinear processes (2005)
  5. Pei, Jin-Song; Wright, Joseph P.; Smyth, Andrew W.: Mapping polynomial fitting into feedforward neural networks for modeling nonlinear dynamic systems and beyond (2005)
  6. Yue, Wenzheng; Tao, Guo: A new type of neural network for reservoir identification using geophysical well logs (2005)
  7. Enăchescu, Denis: Multilayer perceptron model for prostate cancer prediction (2004)
  8. Gao, Fan; Latash, Mark L.; Zatsiorsky, Vladimir M.: Neural network modeling supports a theory on the hierarchical control of prehension (2004) ioport
  9. Gläßel, Holger; Zimmermann, Frank; Brückner, Steffen; Schöttle, Ulrich M.; Rudolph, Stephan: Adaptive neural control of the deployment procedure for tether-assisted re-entry (2004)
  10. Latini, G. (ed.); Passerini, G. (ed.): Handling missing data: applications to environmental analysis. With CD-ROM. (2004)
  11. Pérez, S. González; Fernández Cañete, J.: Neural-network-based stable control by using harmonic analysis (2004) ioport
  12. Şahin, Şule Önsel; Ülengin, Füsun; Ülengin, Burç: Using neural networks and cognitive mapping in scenario analysis: the case of Turkey’s inflation dynamics (2004)
  13. Al-Haik, M. S.; Garmestani, H.; Navon, I. M.: Truncated-Newton training algorithm for neurocomputational viscoplastic model. (2003)
  14. Arulampalam, Ganesh; Bouzerdoum, Abdesselam: A generalized feedforward neural network architecture for classification and regression. (2003) ioport
  15. Derakhshani, Reza; Schuckers, Stephanie A. C.; Hornak, Larry A.; O’Gorman, Lawrence: Determination of vitality from a non-invasive biomedical measurement for use in fingerprint scanners. (2003) ioport
  16. Garczarek, Ursula; Weihs, Claus: Standardizing the comparison of partitions (2003)
  17. Jiang, Xudong; Wah, Alvin Harvey Kam Siew: Constructing and training feed-forward neural networks for pattern classification. (2003) ioport
  18. Karul, C.; Soyupak, S.: A comparison between neural network based and multiple regression models for chlorophyll-a estimation (2003)
  19. Kumar, Ajay: Neural network based detection of local textile defects. (2003) ioport
  20. Laws, Mark; Kilgour, Richard; Kasabov, Nikola: Modeling the emergence of bilingual acoustic clusters: A preliminary case study. (2003) ioport

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