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Cybernetics & Systems is a peer-reviewed journal published by Taylor & Francis, covering Multi-Criteria Decision Making, Fuzzy Logic and Control Systems, Neural Networks and Applications.
| Publisher | Taylor & Francis |
|---|---|
| Reference style | Author–year (Chicago, T&F) Author–year — (Smith, 2023) in the text Smith, Ada, Ben Jones, and Cara Lee. 2023. "A Representative Article Title." Cybernetics & Systems 12 (3): 45–58.
Formats any DOI in Cybernetics & Systems style. No sign-up. |
| Publishes research in | Multi-Criteria Decision Making Fuzzy Logic and Control Systems Neural Networks and Applications Complex Systems and Decision Making Semantic Web and Ontologies |
| ISSN | 0196-9722 |
| Citation impact (2-yr) | 1.6 |
| h-index | 61 |
| i10-index | 585 |
| Total citations | 22,806 |
| Top institutions publishing here | Gdańsk University of Technology |
| Journal website | www.tandfonline.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Abstract This paper gives basic ideas of rough set theory a new approach to data analysis. The lower and upper approximation of a set, the basic operations of the theory, are intuitively explained and formally defined. Some applications of rough set theory are briefly outlined and some future problems are outlined.
Abstract A drawback of existing fuzzy forecasting methods based on fuzzy time series is that they use the first-order fuzzy time series to deal with forecasting problems in which the forecasting results are not good enough. Using a high-order fuzzy time series to deal with fuzzy forecasting problems can overcome this drawback. In this paper,…
Optimal control is a very important field of study not only in theory but in applications, and stochastic optimal control is also a significant branch of research in theory and applications. Based on the concept of uncertain process, an uncertain optimal control problem is dealt with. Applying Bellman's principle of optimality, the principle of optimality…
We describe work in progress with the aim of constructing a computational model of emotional learning and processing inspired by neurophysiological findings. The main brain areas modeled are the amygdala and the orbitofrontal cortex and the interaction between them. We want to show that (1) there exists enough physiological data to suggest the overall architecture…
Natural evolution provides a paradigm for the design of stochastic-search optimization algorithms. Various forms of simulated evolution, such as genetic algorithms and evolutionary programming techniques, have been used to generate machine learning through automated discovery. These methods have been applied to complex combinatorial optimization problems with varied degrees of success. The present paper relates the…
