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. Min, Hyeung-Sik; Yih, Yuehwern: Selection of dispatching rules on multiple dispatching decision points in real-time scheduling of a semiconductor wafer fabrication system (2003)
  2. Wilson, D. Randall; Martinez, Tony R.: The general inefficiency of batch training for gradient descent learning. (2003) ioport
  3. Yang, D.-M.; Stronach, A. F.; MacConnell, P.: The application of advanced signal processing techniques to induction motor bearing condition diagnosis (2003)
  4. Alsing, Stephen G.; Bauer, Kenneth W. jun.; Miller, John O.: A multinomial selection procedure for evaluating pattern recognition algorithms (2002)
  5. Cubiles-de-la-Vega, María-Dolores; Pino-Mejías, Rafael; Pascual-Acosta, Antonio; Muñoz-García, Joaquín: Building neural network forecasting models from time series ARIMA models: a procedure and a comparative analysis (2002)
  6. Goh, W. Y.; Lim, C. P.; Peh, K. K.; Subari, K.: Application of a recurrent neural network to prediction of drug dissolution profiles (2002)
  7. Ikeda, Yuji; Mazurkiewicz, Dariusz: Application of neural network technique to combustion spray dynamics analysis (2002)
  8. Lazar, Mircea; Pastravanu, Octavian: A neural predictive controller for non-linear systems (2002)
  9. Lee, Tae-Seung; Choi, Ho-Jin; Kwag, Young-Kil; Hwang, Byong-Won: A method on improvement of the online mode error backpropagation algorithm for pattern recognition (2002)
  10. Mittra, R.; Suntives, A.; Hossain, M. S.; Ma, J.: A systematic approach for extracting lumped circuit parameters of microstrip discontinuities from their S-parameter characteristics (2002)
  11. Montañés, Elena; Quevedo, José R.; Prieto, Maria M.; Menéndez, César O.: Forecasting time series combining machine learning and Box-Jenkins time series (2002)
  12. Tran-Canh, D.; Tran-Cong, T.: BEM-NN computation of generalised Newtonian flows (2002)
  13. Waszczyszyn, Zenon; Bartczak, Marek: Neural prediction of buckling loads of cylindrical shells with geometrical imperfections (2002)
  14. Zhou, Zhi-Hua; Wu, Jianxin; Tang, Wei: Ensembling neural networks: Many could be better than all (2002)
  15. Aarts, Lucie P.; van der Veer, Peter: Neural network method for solving partial differential equations (2001)
  16. Amato, F.; Mattei, M.: Robust control of a plasma wind tunnel: an LPV discrete-time system depending on fast/slowly-varying parameters (2001)
  17. Ambrosino, G.; Celentano, G.; Mattei, M.: A control design oriented mathematical model for the Scirocco plasma wind tunnel (2001)
  18. Chiang, L. H.; Russell, E. L.; Braatz, R. D.: Fault detection and diagnosis in industrial systems (2001)
  19. Dadhe, Kai; Roßmann, Volker; Durmus, Kazim; Engell, Sebastian: Neural networks as a tool for gray box modelling in reactive distillation (2001)
  20. MacLeod, Christopher; Maxwell, Grant M.: Incremental evolution in ANNs: Neural nets which grow (2001)

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