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.
This paper proposes a novel approach for modelling complex interconnected systems by means of fuzzy networks with feedback rule bases. The nodes in these networks are rule bases connected in a feedback manner whereby outputs from some rule bases are fed as inputs to the same or preceding rule bases. The approach allows any fuzzy network of this type to be presented as an equivalent fuzzy system by linguistic composition of its nodes. The composition process makes use of formal models for fuzzy networks, basic operations in such networks, their properties and advanced operations. These models, operations and properties are used for defining several types of networks with single or multiple local and global feedback. The proposed approach facilitates the understanding of complex interconnected systems by improving the transparency of their models.
This paper proposes a rule base simplification method for fuzzy systems. The method is based on aggregation of rules with different linguistic values of the output for identical permutations of linguistic values of the inputs which are known as inconsistent rules. The simplification removes the redundancy in the fuzzy rule base by replacing each group of inconsistent rules with a single equivalent rule. The simulation results from a transportation demand management case study show that the aggregated fuzzy system with the consistent rule base approximates better the given data than the original fuzzy system with the inconsistent rule base. The main advantage of the proposed method over other methods is that it does not require any refinement of the rule base using additional data sets or expert knowledge. In this context, the method is quite suitable for applications where rule base refinement is unacceptable due to time constraints or impossible due to lack of additional data or knowledge.
Quality service of public transport is an important issue in transit systems as users' needs and expectations have great relevance in transit modelling and implementation. This explanatory study examines young men and women's expectations and perceptions regarding the transit quality of service by using the concepts of Zone of Tolerance (ZoT) and adequate and desired levels of service. Due to the critical role of Intelligent Transportation Systems (ITS) in public transport service enhancement, specific dimension, which reflects users' perceptions and expectations over ITS, are also considered. ZoTs for men and women are obtained separately and the results show interesting differences, particularly women's minimum acceptable and desired levels are higher than men and they have relatively bigger ZoTs.
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