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.

Keywords for this software

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References in zbMATH (referenced in 9949 articles , 8 standard articles )

Showing results 1 to 20 of 9949.
Sorted by year (citations)

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  1. Shafai, Bahram: System identification and adaptive control (to appear) (2024)
  2. Abdi, A.; Jackiewicz, Z.: Towards a code for nonstiff differential systems based on general linear methods with inherent Runge-Kutta stability (2019)
  3. Adamatzky, Andrew (ed.); Akl, Selim G. (ed.); Sirakoulis, Georgios Ch. (ed.): From parallel to emergent computing (2019)
  4. Ahmadi, Amir Ali; Majumdar, Anirudha: DSOS and SDSOS optimization: more tractable alternatives to sum of squares and semidefinite optimization (2019)
  5. Ahookhosh, Masoud; Neumaier, Arnold: An optimal subgradient algorithm with subspace search for costly convex optimization problems (2019)
  6. Al-Araji, Ahmed S.: An adaptive swing-up sliding mode controller design for a real inverted pendulum system based on culture-bees algorithm (2019)
  7. Albrecht, Gudrun; Caliò, Franca; Miglio, Edie: Geometrically constrained surface (re)construction (2019)
  8. Ali, M. Syed; Yogambigai, J.; Saravanan, S.; Elakkia, S.: Stochastic stability of neutral-type Markovian-jumping BAM neural networks with time varying delays (2019)
  9. Alsmadi, Yazan M.; Abdel-hamed, Alaa M.; Ellissy, Abo Eleyoun; El-Wakeel, Amged S.; Abdelaziz, Almoataz Y.; Utkin, Vadim; Uppal, Ali Arshad: Optimal configuration and energy management scheme of an isolated micro-grid using cuckoo search optimization algorithm (2019)
  10. Altun, Yener; Tunç, Cemil: On the estimates for solutions of a nonlinear neutral differential system with periodic coefficients and time-varying lag (2019)
  11. Andersson, Joel A. E.; Gillis, Joris; Horn, Greg; Rawlings, James B.; Diehl, Moritz: CasADi: a software framework for nonlinear optimization and optimal control (2019)
  12. Andreas Nüßing, Maria Carla Piastra, Sophie Schrader, Tuuli Miinalainen, Heinrich Brinck, Carsten H. Wolters, Christian Engwer: duneuro - A software toolbox for forward modeling in neuroscience (2019) arXiv
  13. Angelini, Elena: Waring decompositions and identifiability via Bertini and Macaulay2 software (2019)
  14. An, Qi; Jiang, Weihua: Spatiotemporal attractors generated by the Turing-Hopf bifurcation in a time-delayed reaction-diffusion system (2019)
  15. Ansari, K. Akbar; Dichone, Bonni: An introduction to numerical methods using MATLAB (2019)
  16. Aràndiga, Francesc; Yáñez, Dionisio F.: Third-order accurate monotone cubic Hermite interpolants (2019)
  17. Arangala, Crista: Exploring linear algebra. Labs and projects with MATLAB (2019)
  18. Arbabi, Hassan; Mezić, Igor: Prandtl-Batchelor theorem for flows with quasiperiodic time dependence (2019)
  19. Averbuch, Amir Z.; Neittaanmäki, Pekka; Zheludev, Valery A.: Spline and spline wavelet methods with applications to signal and image processing. Volume III. Selected topics (2019)
  20. Baccouch, Mahboub; Kaddeche, Slim: Efficient Chebyshev pseudospectral methods for viscous Burgers’ equations in one and two space dimensions (2019)

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