Employing zSlices based general type-2 fuzzy sets to model multi level agreement

In this paper, we introduce the concept of Multi Level Agreement (MLA) based on (zSlices based) general type-2 fuzzy sets. We define the notion of MLA and describe how it can be computed based on a series of interval type-2 fuzzy sets. We provide examples, visualizing the nature of MLA sets and discuss their properties and interpretation. Moreover, we specifically address the reason for introducing MLA in order to allow the modeling of agreement in real world applications using fuzzy sets while still maintaining an uncertainty model and show that the use of general type-2 fuzzy sets is essential for MLA as classical sets, type-1 and interval type-2 fuzzy sets do not provide a degree of freedom which could be employed to model agreement. © 2011 IEEE

Type-2 fuzzy sets applied to multivariable self-organizing fuzzy logic controllers for regulating anesthesia

In this paper, novel interval and general type-2 self-organizing fuzzy logic controllers (SOFLCs) are proposed for the automatic control of anesthesia during surgical procedures. The type-2 SOFLC is a hierarchical adaptive fuzzy controller able to generate and modify its rule-base in response to the controller's performance. The type-2 SOFLC uses type-2 fuzzy sets derived from real surgical data capturing patient variability in monitored physiological parameters during anesthetic sedation, which are used to define the footprint of uncertainty (FOU) of the type-2 fuzzy sets. Experimental simulations were carried out to evaluate the performance of the type-2 SOFLCs in their ability to control anesthetic delivery rates for maintaining desired physiological set points for anesthesia (muscle relaxation and blood pressure) under signal and patient noise. Results show that the type-2 SOFLCs can perform well and outperform previous type-1 SOFLC and comparative approaches for anesthesia control producing lower performance errors while using better defined rules in regulating anesthesia set points while handling the control uncertainties. The results are further supported by statistical analysis which also show that zSlices general type-2 SOFLCs are able to outperform interval type-2 SOFLC in terms of their steady state performance

Novel methods for the design of general type-2 fuzzy sets based on device characteristics and linguistic labels surveys

Fuzzy Logic Systems are widely recognized to be successful at modelling uncertainty in a large variety of applications. While recently interval type-2 fuzzy logic has been credited for the ability to better deal with large amounts of uncertainty, general type-2 fuzzy logic has been a steadily growing research area. All fuzzy logic systems require the accurate specification of the membership functions' (MFs) parameters. While some work for automatic or manual design of these parameters has been proposed for type-1 and interval type-2 fuzzy logic, the problem has not yet been widely addressed for general type-2 fuzzy logic. In this paper we propose two methods which allow the automatic design of general type-2 MFs using either data gathered through a survey on the linguistic variables required or, in the case of physical devices (e.g. sensors, actuators), using data directly gathered from the specific devices. As such, the proposed methods allow for the creation of general type-2 MFs which directly model the uncertainty incorporated in the respective applications. Additionally, we demonstrate how interval type-2, type-1 and the recently introduced zSlices based general type-2 MFs can be extracted from the automatically designed general type-2 MFs. We also present a recursive algorithm that computes the convex approximation of generated fuzzy sets