FL RBC’s Parameters
FLS is one of the common approaches to soft computing methods. Since its first visit in the 1970s, it has been unromantic in many areas. As a formal description, a fuzzy logic system (or fuzzy system) is a computing system in which a fuzzy set is used for the source and/or the result of a fuzzy IF-THEN rule. Likewise, the attractiveness and success of FLS comes from the core features it pursues:
- • Ability to flexibly represent objects with fuzzy sets
- • Interpretability of results by providing insight into the visualization production process.
J. Mendel proposed a new direction for fuzzy system research that considers type 2 fuzzy sets (T2 FS) in rule wiring instead of using the usual fuzzy set (type 1 fuzzy set - T1 FS). By definition, T2FS is a fuzzy set, and the membership level of each element is T1FS of [0,1]. Therefore, the membership function of T2 FS is three-dimensional. The benefit of this flow is theoretically reducing the impact of uncertainty on fuzzy logic systems. In particular, J. Mendel emphasized the health of T2 FS in that it is worth dealing with potential noise in the training data. There has been a lot of works in the past decade on T2 FS and T2 FLS both in theoretical and practical terms. Some of the main approaches are as follows:
- • First, reinforce the basic theory towards a well-formed T2 FLS with computational skills for T2 FS such as FOU concept, expression theorem, set theory operation, centroid, inference, and type reduction.
- • Second, apply T2 FS and T2 FLS to perceptual modeling, perceptual computing, and word computing.
- • Finally, in areas such as control and medicine, training data demonstrates the effectiveness of T2 FLS in problems such as noisy or uncertain predictions, and classifications.
Recently, a new entrance ceremony of type 2 fuzzy set-hedging trigonometry type 2 fuzzy set (HaT2FS) has been proposed. Apparently, HaT2FS is a fuzzy set with language membership levels. However, the flow of HaT2FS varies fundamentally from study to study. Instead of traditional processing, HaT2FS's numbering and reasoning works based on the nature of the linguistic truth values of hedging algebra.
To prove the applicability of the hedge trigonometric type 2 fuzzy logic system (HaT2-FLS), and to take enough of the HaT2FS sound battleship, you can apply the experimental method after applying the method of constructing HaT2-FLS from input and output data. Among knowledge-based systems research, the pattern naming problem is one of the most important things. The key to this problem is that there is a possibility that there will be uncertainty in the data (i.e. the data contains noise). There are many different approaches to deal with this problem, from statistical-based techniques such as the subconscious Markov model to soft computing methods such as tense neural networks and fuzzy rules-based naming systems. Among them, the contribution of fuzzy logic systems to the problem is highly appreciated.
