Context adaptation can be achieved by adjusting an initial normalized fuzzy rule-based system through the use of operators that appropriately change the representation of the linguistic variables. The choice of the specific operators and their parameters should be context-based and optimized so as to obtain a good interpretability-accuracy tradeoff. In this paper we propose a set of context adaptation operators that, starting from a given fuzzy system, adjust some of its component!, such as fuzzy set support and core, membership function shape, etc. We use a genetic tuning process for choosing the operator parameters. We finally describe the application of the proposed operators to Mamdani fuzzy systems with reference to two real examples.
Context Adaptation of Mamdani Fuzzy Systems through New Operators Tuned by a Genetic Algorithm
LAZZERINI, BEATRICE;MARCELLONI, FRANCESCO
2006-01-01
Abstract
Context adaptation can be achieved by adjusting an initial normalized fuzzy rule-based system through the use of operators that appropriately change the representation of the linguistic variables. The choice of the specific operators and their parameters should be context-based and optimized so as to obtain a good interpretability-accuracy tradeoff. In this paper we propose a set of context adaptation operators that, starting from a given fuzzy system, adjust some of its component!, such as fuzzy set support and core, membership function shape, etc. We use a genetic tuning process for choosing the operator parameters. We finally describe the application of the proposed operators to Mamdani fuzzy systems with reference to two real examples.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
