Modular rule base fuzzy networks for linguistic composition based modelling

Linguistic composition based modelling by fuzzy networks with modular rule bases

Model Optimisation for Complex Systems using Fuzzy Networks Theory

This paper presents an application of the novel theory of fuzzy networks for optimising models of systems characterised by uncertainty, non-linearity, modular structure and interactions. The application of the theory is demonstrated for retail price models in the context of converting a multiple rule base fuzzy system (MRBFS) into an equivalent single rule base fuzzy system (SRBFS) by linguisti...

Advanced Modelling of Complex Processes by Fuzzy Networks

This work presents an application of the novel theory of rule based networks for building models of processes characterised by uncertainty, non-linearity, modular structure and internal interactions. The application of the theory is demonstrated for a flotation process in the context of converting a multiple rule based system into an equivalent single rule based system by linguistic composition...

Modelling Expectations with GENEFER - an Artificial Intelligence approach

Economic modelling of financial markets means to model highly complex systems in which expectations can be the dominant driving forces. Therefore it is necessary to focus on how agents form their expectations. We believe that they look for patterns, hypothesize, try, make mistakes, learn and adapt. Agents’ bounded rationality leads us to a rule-based approach which we model using Fuzzy Rule-Bas...

Modeling Expectations with GENEFER - an Artificial Intelligence approach

Economic modeling of financial markets attempts to model highly complex systems in which expectations can be among the dominant driving forces. It is necessary, then, to focus on how agents form expectations. We believe that they look for patterns, hypothesize, try, make mistakes, learn and adapt. Agents’ bounded rationality leads us to a rule-based approach which we model using Fuzzy Rule Base...