Abstract
Fuzzy knowledge-based systems (FKBS) are significantly applicable in the area of control, classification, and modeling, having knowledge in the form of fuzzy if-then rules. Type-2 fuzzy theory is used to make these systems more capable of dealing with inherent uncertainties in real-world problems. In this paper, the authors have proposed a genetic tuning approach named lateral displacement and expansion/compression (LDEC) in which α and β parameters are calculated to adjust the parameters of interval type-2 membership functions. α tuning deals with lateral displacement, whereas β tuning carries out compression/expansion operation. The interpretability and accuracy features are considered during the development of this approach. The experimental results show the performance of the proposed approach.
Highlights
Fuzzy systems, fuzzy knowledge-based systems (FKBS) or fuzzy rulebased systems (FRBS), are significantly applicable in areas like control [1], classification [2], and modeling [3]
data base (DB) is the repository of membership functions (MFs) and scaling functions (SFs) representing linguistic values, whereas rule base (RB) is the collection of knowledge related to problems in terms of fuzzy if- rules
Type-2 fuzzy systems are strongly capable of modeling uncertainties in FKBS than type1 fuzzy systems using three-dimensional membership function representation
Summary
Fuzzy knowledge-based systems (FKBS) or fuzzy rulebased systems (FRBS), are significantly applicable in areas like control [1], classification [2], and modeling [3]. Genetic algorithms (GAs) are used for learning and tuning of various parameters of KB due to their strong capacity of searching in a complicated and poorly defined search space Such an application of GAs in developing FKBS is named as genetic fuzzy systems (GFS) [5,6,7,8]. In [53], the performance of a fuzzy rule-based classification system is improved using an interval-valued fuzzy set and a tuning approach using genetic algorithm. Type-1 fuzzy system implementation The values of accuracy and interpretability measures calculated in the following experiments are given in Table 4 and Figure 12: Experiment 1 (E1) Fuzzy partition method: hierarchical fuzzy partition (HFP) and rule generation method: Wang-Mendel method. Experiment 4 (E4) Fuzzy partition method: SFP and rule generation method: Wang-Mendel method
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