Fuzzy Networks: Analysis and Design


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.


Fuzzy networks have an underlying two-dimensional grid structure with horizontal levels and vertical layers. The levels represent spatial hierarchy in terms of network breadth and the layers represent temporal hierarchy in terms of network depth. The nodes of fuzzy networks are modelled by Boolean matrices or binary relations. The connections between the nodes are modelled by block schemes or topological expressions. Each network node is located in a cell within the underlying grid structure. Nodes in fuzzy networks are manipulated by merging and splitting operations. The merging operations are for network analysis and the splitting operations are for network design. These operations are used for converting a fuzzy network into a fuzzy system and vice versa. The operations are illustrated on feedforward and feedback fuzzy networks. Feedforward networks include combinations of narrow/broad and shallow/deep network structures. Feedback networks include combinations of single/multiple and local/global feedback loops. Fuzzy networks are applied to case studies from engineering, computing, transport and finance. They are validated successfully against standard and hierarchical fuzzy systems. The validation uses performance evaluation indicators for feasibility, accuracy, efficiency, transparency.


Alexander Gegov

https://www.port.ac.uk/about-us/structure-and-governance/our-people/our-staff/alexander-gegov Alexander Gegov is Reader in Computational Intelligence in the School of Computing, University of Portsmouth, UK. He holds a PhD degree in Cybernetics and a DSc degree in Artificial Intelligence – both from the Bulgarian Academy of Sciences. He has been a recipient of a National Annual Award for Best Young Researcher from the Bulgarian Union of Scientists. He has been Humboldt Guest Researcher at the University of Duisburg in Germany. He has also been EU Visiting Researcher at the University of Wuppertal in Germany and the Delft University of Technology in the Netherlands. Alexander Gegov’s research interests are in the development of computational intelligence methods and their application for modelling and simulation of complex systems and networks. He has edited 6 books, authored 5 research monographs and over 30 book chapters – most of these published by Springer. He has authored over 50 articles and 100 papers in international journals and conferences – many of these published and organised by IEEE. He has also presented over 20 invited lectures and tutorials – most of these at IEEE Conferences on Fuzzy Systems, Intelligent Systems, Computational Intelligence and Cybernetics. Alexander Gegov is Associate Editor for ‘IEEE Transactions on Fuzzy Systems’, ‘Fuzzy Sets and Systems’, ‘Intelligent and Fuzzy Systems’, ‘Computational Intelligence Systems’ and ‘Intelligent Systems’. He is also Reviewer for several IEEE journals and National Research Councils. He is Member of the IEEE Computational Intelligence Society and the Soft Computing Technical Committee of the IEEE Society of Systems, Man and Cybernetics. He has also been Guest Editor for a recent Special Issue on Deep Fuzzy Models of the IEEE Transactions on Fuzzy Systems.

Farzad Arabikhan

https://www.port.ac.uk/about-us/structure-and-governance/our-people/our-staff/farzad-arabikhan Farzad Arabikhan joined the University of Portsmouth, UK, as a Lecturer in 2017 after completing his PhD on Modelling Telecommuting using Fuzzy Networks at the same university. He has published his research results in several journal articles and conference papers. He has also secured funding from the EU COST Programme for research collaboration with leading academics at the Paris-Sorbonne University, France and the Mediterranean University of Reggio Calabria, Italy. He holds BSc and MSc degrees in Civil Engineering and Transportation Engineering from the Sharif University of Technology, Tehran, Iran.


[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