Expertise and uncertainty processing with nonlinear scaling and fuzzy systems for automation
|Author:||Juuso, Esko K.1|
1Control Engineering, University of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.5 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020100983548
|Publish Date:|| 2020-10-09
Integration of domain expertise and uncertainty processing is increasingly important in automation solutions which rely on data analytics and artificial intelligence. We need a level to assess what is approximately correct. Uncertainties of the inputs are taken into account by using fuzzy numbers as the inputs of different fuzzy and parametric systems. Nonlinear scaling functions (NSFs) integrate these solutions and make them easier to tune. Fuzzy rule-based systems are represented with scaled fuzzy inputs. Membership functions (MFs) can be developed from NSFs and existing MFs can be used in developing NSFs. Fuzzy set systems and linguistic equation (LE) systems become consistent within the limits of detail. In recursive analysis, both meanings and interactions on all levels can be tuned together with genetic algorithms. In applications, the modular overall system consists of similar subsystems, which are normally used, with extensions to fuzzy. The compact fuzzy modules can be developed for specific tasks which are combined within Cyber Physical Systems (CPS). Uncertainty processing is embedded in the recursive analysis. The fuzzy extensions provide a feasible way for the sensitivity analysis of the solution.
|Pages:||712 - 720|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
113 Computer and information sciences
© 2020 E. K. Juuso, published by De Gruyter. This work is licensed under the Creative Commons Attribution 4.0 International License. BY 4.0.