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Five-instant type discrete-time ZND solving discrete time-varying linear system, division and quadratic programming
- Source :
- Neurocomputing. 331:323-335
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
Abstract
- Discrete time-varying linear system (LS) is a fundamental topic in science and engineering. However, conventional methods essentially designed for time-invariant LS generally assume that LS is time-invariant during a small time interval (i.e., sampling gap) for solving time-varying LS. This assumption quite limits their precision because of the existing of lagging errors. Discarding this assumption, Zhang neural dynamics (ZND) method improves the precision for LS solving, which is a great alternative for the solving of discrete time-varying problems. Note that precision solutions to discrete time-varying problems depend on discretization formulas. In this paper, we propose a new ZND model to solve the discrete time-varying LS. The discrete time-varying division is a special case of discrete time-varying LS with the solution being a scalar while it is usually studied alone. Considering the above inner connection, we further propose a special model for solving the discrete time-varying division. Moreover, as an application of discrete time-varying LS, the discrete time-varying quadratic programming (QP) subject to LS is also studied. The convergence and precision of proposed models are guaranteed by theoretical analyses and substantiated by numerous numerical experiments.
- Subjects :
- 0209 industrial biotechnology
Discretization
Cognitive Neuroscience
Science and engineering
Scalar (mathematics)
Linear system
02 engineering and technology
Computer Science Applications
020901 industrial engineering & automation
Discrete time and continuous time
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
Applied mathematics
020201 artificial intelligence & image processing
Quadratic programming
Special case
Mathematics
Instant
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 331
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi...........eccea4eaa44413831da04b0c44b3d250
- Full Text :
- https://doi.org/10.1016/j.neucom.2018.11.064