Abstract
Abstract 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.
Highlights
Integration of domain expertise and uncertainty processing is increasingly important in automation solutions which rely on data analytics and artificial intelligence
Domain expertise and uncertainty processing are increasingly important in automation solutions which rely on data analytics and artificial intelligence
The operating area of different types of rule-based fuzzy set systems can be extended by selecting membership locations (Figure 2) for inputs and/or outputs together with the linguistic equation (LE) models are needed to tune the sets of Membership functions (MFs), see Section 2.3.3
Summary
Domain expertise and uncertainty processing are increasingly important in automation solutions which rely on data analytics and artificial intelligence. Adaptation, which is essential in varying operating conditions, is an important part of fuzzy systems: scaling, modifying membership functions and updating rules are supported in many ways. Self-organising maps (SOM), which have many alternatives for calculating distances in the competitive layer [10], are suitable for clustering and shaping fuzzy rule-based systems. Linguistic equation (LE) systems originate from fuzzy systems [13]: nonlinear scaling makes the solutions compact by facilitating the use of linear methodologies for. This paper analyses alternatives of the fuzzy extensions of parametric systems. Different types fuzzy and parametric systems are building blocks of these systems (Section 2). The α-cuts of higher membership degrees are always subsets of the α-cuts of lower membership degrees:
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