Matlab

MATLAB® is a high-level language and interactive environment for numerical computation, visualization, and programming. Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java™. You can use MATLAB for a range of applications, including signal processing and communications, image and video processing, control systems, test and measurement, computational finance, and computational biology. More than a million engineers and scientists in industry and academia use MATLAB, the language of technical computing.

This software is also referenced in ORMS.


References in zbMATH (referenced in 6319 articles , 8 standard articles )

Showing results 1 to 20 of 6319.
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  1. Alonso, Pedro; Ibáñez, Javier; Sastre, Jorge; Peinado, Jesús; Defez, Emilio: Efficient and accurate algorithms for computing matrix trigonometric functions (2017)
  2. Chuong, T.D.; Jeyakumar, V.: A generalized Farkas lemma with a numerical certificate and linear semi-infinite programs with SDP duals (2017)
  3. Diaz-Toca, Gema M.; Belhaj, Skander: Blind image deconvolution through Bezoutians (2017)
  4. Duffy, Dean G.: Advanced engineering mathematics with MATLAB (to appear) (2017)
  5. Duriez, Thomas; Brunton, Steven L.; Noack, Bernd R.: Machine learning control -- taming nonlinear dynamics and turbulence (2017)
  6. Fawzi, Hamza; Saunderson, James: Lieb’s concavity theorem, matrix geometric means, and semidefinite optimization (2017)
  7. Gao, Qing: Universal fuzzy controllers for non-affine nonlinear systems (2017)
  8. Herman, Russell L.: An introduction to Fourier analysis (2017)
  9. Lin, Hong; Su, Hongye; Shi, Peng; Shu, Zhan; Wu, Zheng-Guang: Estimation and control for networked systems with packet losses without acknowledgement (2017)
  10. Monti, A.; Ponci, F.; Riva, M.: Electrical machine theory through finite element analysis. (to appear) (2017)
  11. Rogers, Simon; Girolami, Mark: A first course in machine learning (2017)
  12. Shafai, Bahram: System identification and adaptive control (to appear) (2017)
  13. Sun, Jiguang; Zhou, Aihui: Finite element methods for eigenvalue problems (2017)
  14. Aavatsmark, Ivar: Interpretation of well-cell pressures on hexagonal grids in numerical reservoir simulation (2016)
  15. Adam, Lukáš; Branda, Martin: Nonlinear chance constrained problems: optimality conditions, regularization and solvers (2016)
  16. Adam, Stavros P.; Magoulas, George D.; Karras, Dimitrios A.; Vrahatis, Michael N.: Bounding the search space for global optimization of neural networks learning error: an interval analysis approach (2016)
  17. Adam, Stefan: Competent use of MATLAB and mathematics. An introduction for engineers and scientists (2016)
  18. Alessio, Silvia Maria: Digital signal processing and spectral analysis for scientists. Concepts and applications (2016)
  19. Alfa, Attahiru S.: Applied discrete-time queues (2016)
  20. Al-Hdaibat, B.; Govaerts, W.; Kuznetsov, Yu.A.; Meijer, H.G.E.: Initialization of homoclinic solutions near Bogdanov-Takens points: Lindstedt-Poincaré compared with regular perturbation method (2016)

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