Tutorial - Fuzzy Networks: Analysis and Design

Alexander Gegov,University of Portsmouth, UK; Farzad Arabikhan, University of Portsmouth, UK ABSTRACT: The tutorial focuses on the analysis and design of fuzzy networks. It highlights some recent research results of the presenters as the ones from the publications listed below. Fuzzy networks are similar to neural networks in terms of general structure. However, their nodes and connections are different. The nodes of fuzzy networks are fuzzy systems represented by rule bases and the connections between the nodes are outputs from and inputs to these rule bases. In this context, apart from being a structural counterpart for a neural network, a fuzzy network is also a conceptual generalisation of a fuzzy system.[1] A.Gegov, Fuzzy Networks for Complex Systems: A Modular Rule Base Approach, Series in Studies in Fuzziness and Soft Computing (Springer, Berlin, 2011) [2] F.Arabikhan, Telecommuting Choice Modelling using Fuzzy Rule Based Networks, PhD Thesis (University of Portsmouth, UK, 2017)[3] A.Gegov, F.Arabikhan and N.Petrov, Linguistic composition based modelling by fuzzy networks with modular rule bases, Fuzzy Sets and Systems 269 (2015) 1-29[4] X.Wang, A.Gegov, F.Arabikhan, Y.Chen and Q.Hu, Fuzzy network based framework for software maintainability prediction, Uncertainty, Fuzziness and Knowledge Based Systems 27/5 (2019) 841-862[5] A.Yaakob, A.Serguieva and A.Gegov, FN-TOPSIS: Fuzzy networks for ranking traded equities, IEEE Transactions on Fuzzy Systems 25/2 (2016) 315-332[6] A.Yaakob, A.Gegov and S.Rahman, Fuzzy networks with rule base aggregation for selection of alternatives, Fuzzy Sets and Systems 341 (2018) 123-144