Theory of T-norms and fuzzy inference methods
Fuzzy Sets and Systems - Special memorial volume on fuzzy logic and uncertainly modelling
Design of fuzzy logic controllers based on generalized T-operators
Fuzzy Sets and Systems - Special memorial volume on fuzzy logic and uncertainly modelling
Fuzzy sets in approximate reasoning, part 1: inference with possibility distributions
Fuzzy Sets and Systems - Special memorial volume on foundations of fuzzy reasoning
Predictive fuzzy expert controllers
Computers and Industrial Engineering
Fuzzy control and fuzzy systems (2nd, extended ed.)
Fuzzy control and fuzzy systems (2nd, extended ed.)
An introduction to fuzzy control
An introduction to fuzzy control
Tuning cascade PID controllers using fuzzy logic
MIM-S2 '93 Papers from the symposium on Second mathematical and intelligent models in system simulation
Fuzzy and Neural Approaches in Engineering
Fuzzy and Neural Approaches in Engineering
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Reasoning in Information, Decision, and Control Systems
Fuzzy Reasoning in Information, Decision, and Control Systems
Intelligent Control: Aspects of Fuzzy Logic and Neural Nets
Intelligent Control: Aspects of Fuzzy Logic and Neural Nets
Soft Computing and Fuzzy Logic
IEEE Software
Responses to Elkan (Lotfi A. Zadeh)
IEEE Expert: Intelligent Systems and Their Applications
CONTROL'05 Proceedings of the 2005 WSEAS international conference on Dynamical systems and control
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Various techniques have been used to control or stabilize economic processes:from econometric models based on rational expectations (assuming that shiftsin economic policy produce revised expectations of rational agents), toregulatory mechanisms, normally regarded as engineering tools. However,applying nonconventional techniques, such as fuzzy control, could provide moreflexible alternatives to the conventional way. Presumably, the interest inapplying fuzzy control to economic processes consists of at least twoadvantages: on one hand in prescribing control actions by linguisticdescriptions and on the other hand in the capability of transition from linearto nonlinear modes of control, conjugated with fine-tuning procedures. Thispaper is intended to exemplify the latter opportunity. After brieflydescribing certain fundamental concepts of fuzzy control, we focus on thedesign of fuzzy linear controllers that emulate conventional modes of controlin the first stage and on the refinement of such fuzzy controllers by makingthem progressively nonlinear in the second stage. As an application of fuzzycontrol to economic processes, we provide a fuzzy extension of the Phillips'stabilization model in two variants: for a closed economy as well as for anopen economy. To implement the control schemata, tools like Matlab andSimulink are used.