Back to Search
Start Over
Neural networks with finite-time convergence for solving time-varying linear complementarity problem
- Source :
- Neurocomputing. 439:146-158
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- Time-varying linear complementarity problem (TLCP) has received a great deal of attention due to its broad variety of scientific and engineering applications. Several efficient Zhang neural networks are introduced for solving TLCP in this paper. Theoretical analysis shows that the related error function of the model proposed in this paper eventually tends to zero. The state convergence time periods of those Zhang neural networks with three types of activation functions are proved to be finite and can be quantitatively estimated by using some given parameters. Further, it is shown that the proposed neural network is of noise-tolerance, which means the neural network is more appropriate for a wider application. Moreover, in order to implement neural network numerically, a related discrete-time version is also studied. Finally, numerical simulations confirm the analysis of the proposed models concretely.
- Subjects :
- 0209 industrial biotechnology
Artificial neural network
Computer science
Cognitive Neuroscience
Zero (complex analysis)
02 engineering and technology
State (functional analysis)
Linear complementarity problem
Computer Science Applications
Error function
020901 industrial engineering & automation
Artificial Intelligence
Convergence (routing)
0202 electrical engineering, electronic engineering, information engineering
Applied mathematics
020201 artificial intelligence & image processing
Variety (universal algebra)
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 439
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi...........0da5443e9b19d08da9bfb5175bebba6b