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. Szczuka, Marcin; Wojdyłło, Piotr: Neuro-wavelet classifiers for EEG signals based on rough set methods (2001)
  2. Ma, Tsao Tsung; Lo, Kwok Lun; Tumay, Mehmet: A robust UPFC damping control scheme using PI and ANN based adaptive controllers (2000)
  3. Pal, S. K.; Saxena, P. K.: Enhancement of highly noisy speech signals (2000)
  4. Moshiri, Saeed; Cameron, Norman E.; Scuse, David: Static, dynamic, and hybrid neural networks in forecasting inflation (1999)
  5. Reyes-Aldasoro, C. C.; Ganguly, A. R.; Lemus, G.; Gupta, A.: A hybrid model based on dynamic programming, neural networks, and surrogate value for inventory optimisation applications (1999)
  6. Eriksson, Jerry; Gulliksson, Mårten; Lindström, Per; Wedin, Per-Åke: Regularization tools for training large feed-forward neural networks using automatic differentiation (1998)
  7. Maguire, L. P.; Roche, B.; McGinnity, T. M.; McDaid, L. J.: Predicting a chaotic time series using a fuzzy neural network (1998)
  8. Malinowski, Tomasz; Kosiński, Robert A.: Applications of neural networks to the analysis of dynamics of nonlinear spatially extended systems (1998)
  9. Rudnicki, Marek; Neittaanmäki, Pekka; Jokinen, Tapani: Neural network simulation of a pulse magnetiser for magnetising permanent magnets (1998)
  10. Dai, Hengchang; MacBeth, Colin: Effects of learning parameters on learning procedure and performance of a BPNN. (1997) ioport
  11. Dai, Hengchang; MacBeth, Colin: Effects of learning parameters on learning procedure and performance of a BPNN. (1997) ioport
  12. Guardabassi, Guido O.; Savaresi, Sergio M.: Approximate feedback linearization of discrete-time non-linear systems using virtual input direct design (1997)
  13. Karniel, Amir; Inbar, Gideon F.: A model for learning human reaching movements (1997)
  14. Magoulas, G. D.; Vrahatis, M. N.; Androulakis, G. S.: Effective backpropagation training with variable stepsize. (1997) ioport
  15. Teixeira, João C.; Rodrigues, Antonio J.: An applied study on recursive estimation methods, neural networks and forecasting (1997)
  16. Wu, Zhi Qiao; Harris, Chris J.: A neurofuzzy network structure for modelling and state estimation of unknown nonlinear systems (1997)
  17. Johansen, Tor A.: Identification of nonlinear systems using empirical data and prior knowledge -- An optimization approach (1996)
  18. Karayanakis, N. M.: Advanced system modelling and simulation with block diagram languages (1995)

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