Linguistic composition based modelling by fuzzy networks with modular rule bases

Abstract

This paper proposes a linguistic composition based modelling approach by networked fuzzy systems that are known as fuzzy networks. The nodes in these networks are modules of fuzzy rule bases and the connections between these modules are the outputs from some rule bases that are fed as inputs to other rule bases. The proposed approach represents a fuzzy network as an equivalent fuzzy system by linguistic composition of the network nodes. In comparison to the known multiple rule base approaches, this networked rule base approach reflects adequately the structure of the modelled process in terms of interacting sub-processes and leads to more flexible solutions. The approach improves significantly the transparency of the associated model while ensuring a high level of accuracy that is comparable to the one achieved by established approaches. Another advantage of this fuzzy network approach is that it fits well within the existing approaches with single rule base and multiple rule bases.

Selection of alternatives using fuzzy networks with rule base aggregation

“…Next, using Eq. (7) for and , the distance between E1 according to DM1 and the FPIS is calculated as:…”

“…The following Table 1 and Table 2 are used by decision makers to evaluate the rating of alternatives and the importance of criteria, and Table 3 is used to determine the alternative level as the output, in generating fuzzy rule bases. (1, 3, 3, 5) Fair (F) 4 (3, 5, 5, 7) Medium Good (MG) 5 (5,7,7,9) Good (G) 6 (7, 9, 9, 10) Very Good (VG) 7 (9, 10, 10, 10) The following are the procedures involved in implementing a fuzzy network with merging rule bases to TOPSIS, based on Type 1-fuzzy set. Steps 1-6 are adopted from [16] and [12], while steps 7-10 are introduced as part of the proposed method in this paper.…”

Modular rule base fuzzy networks for linguistic composition based modelling

“…The linguistic composition approach is applied to a case study on the first stage of an ore flotation process by using available data from the mining industry. This application is similar to the one described in [41] in that it uses the same approach but the case study here is quite The initial part of the rule base for the SFS is shown in Table 1. This rule base is derived from data about the product pricing process and in accordance with Equation (43).…”

Fuzzy networks with feedback rule bases for complex systems modelling

“…This example considers an operand node N with output y and input x that is augmented with an input x AI . This node can be described by the Boolean matrix in Equation (33). In this context, node N represents a one-node FN that can be described by the block-scheme in Figure 8 and the topological expression in Equation (34).…”

“…In this respect, uncertainty is an obstacle to accuracy as it is harder to build an accurate model from uncertain data [29][30][31][32][33][34]. Furthermore, dimensionality represents an obstacle to efficiency because it is more difficult to reduce the amount of computations in a FID sequence for a large number of rules [35][36][37][38][39][40].…”