Abstract
In this paper, we use a PLA model for performance and behaviour analysis of fuzzy system. This model makes connection between fuzzy logic and formalism. A case study contains an illustration how the proposed model can be fruitfully exploited to model traffic control systems based on fuzzy logic. Piece-Linear Aggregate model for traffic signal control system has been transformed into timed automaton for verification of safety, liveness, bounded- liveness and deadlock- freeness properties based on model checking. The system performance analysis was performed using Arena software package. A comparative analysis of traffic light controllers with fixed time and fuzzy logic algorithms is given. DOI: http://dx.doi.org/10.5755/j01.eee.19.1.3261
Highlights
Formal methods are widely used for modelling and verification of complex systems.By the increasing interest in a fuzzy logic a lot of scientists were interested to use formal methods to model fuzzy systems
For formal modelling of fuzzy system we used piece linear- aggregate (PLA) formalism [9], which allows on the base of single formal description of system to create models for performance and behaviour analysis
We propose a general PLA model for modelling fuzzy systems
Summary
Formal methods are widely used for modelling and verification of complex systems. By the increasing interest in a fuzzy logic a lot of scientists were interested to use formal methods to model fuzzy systems. PLA formalism can be defined like a universal and general methodology that provides tools to simulate and verify systems which behaviour is based on discrete event [6]. Each aggregate is represented as an object defined by a set of input signals X, output signals Y, events E and states Z. A fuzzy logic systems (FLS) consists of four main parts: fuzzifier, rules, inference engine, and defuzzifier In PLA approach the fuzzy logic system aggregate is composed of several subaggregates (Distributor, Fuzzifiers, Connector, SMF and Deffuzifier) interacting with each other in order to implement a fuzzy system. A Fuzzifier transforms crisp values into membership grades of fuzzy sets Formal specification of this aggregate in PLA formalism is presented below: 1.
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