Spelling suggestions: "subject:"interval type's fuzzy set"" "subject:"interval type.a fuzzy set""
1 |
An Improved Type Reduction Algorithm for Type-2 Fuzzy SetsSu, Yao-Lung 15 August 2011 (has links)
Type reduction does the work of computing the centroid of a type-2 fuzzy set. The result is a type-1 fuzzy set from which a corresponding crisp number can then be obtained through defuzzification. Type reduction is one of the major operations involved in type-2 fuzzy inference. Therefore, making type reduction efficient is a significant task in the application of type-2 fuzzy systems. Liu introduced a horizontal slice representation, called the £\-plane representation, and proposed a type reduction method for a type-2 fuzzy set. By exploring some useful properties of the £\-plane representation and of the type reduction for interval type-2 fuzzy sets, we develop a fast method for computing the centroid of a type-2 fuzzy set. The number of computations and comparisons involved is greatly reduced. As a result, type reduction can be done much more efficiently. The effectiveness of the proposed method is analyzed mathematically and demonstrated by experimental results.
|
2 |
Geração genética de classificador fuzzy intervalar do tipo-2Pimenta, Adinovam Henriques de Macedo 30 October 2009 (has links)
Made available in DSpace on 2016-06-02T19:05:45Z (GMT). No. of bitstreams: 1
3199.pdf: 1902769 bytes, checksum: 95b9e8c5042bd6117bd75983a58966f6 (MD5)
Previous issue date: 2009-10-30 / Universidade Federal de Sao Carlos / The objective of this work is to study, expand and evaluate the use of interval type-2 fuzzy sets in the knowledge representation for fuzzy inference systems, specifically for fuzzy classifiers, as well as its automatic generation form data sets, by means of genetic algorithms. This work investigates the use of such sets focussing the issue of balance between the cost addition in representation and the gains in interpretability and accuracy, both deriving from the representation and processing complexity of interval type-2 fuzzy sets. With this intent, an evolutionary model composed of three stages was proposed and implemented. In the first stage the rule base is generated, in the second stage the data base is optimized and finally, the number of rules of the rule base obtained is optimized in the third stage. The model developed was evaluated using several benchmark data sets and the results obtained were compared with two other fuzzy classifiers, being one of them generated by the same model using type-1 fuzzy sets and the other one generated by the Wang&Mendel method. Statistical methods usually applied for comparisons in similar contexts demonstrated a significant improvement in the classification rates of the intervalar type-2 fuzzy set classifier generated by the proposed model, with relation to the other methods. / O objetivo deste trabalho é estudar, expandir e avaliar o uso de conjuntos fuzzy intervalares tipo-2 na representação do conhecimento em sistemas de inferência fuzzy, mais especificamente para os classificadores fuzzy, bem como sua geração automática a partir de conjuntos de dados, por meio de algoritmos genéticos. Esse trabalho investiga o uso de tais conjuntos com enfoque na questão de balanceamento entre o acréscimo de custo da representação e os ganhos em interpretabilidade e precisão, ambos decorrentes da complexidade de representação e processamento dos conjuntos fuzzy intervalares do tipo-2. Com este intuito, foi proposto e implementado um modelo evolutivo composto por três etapas. Na primeira etapa á gerada a base de regras, na segunda é otimizada a base de dados e, por fim, na terceira etapa o número de regras da base gerada é otimizado. O modelo desenvolvido foi avaliado em diversos conjuntos de dados benchmark e os resultados obtidos foram comparados com outros dois classificadores fuzzy, sendo um deles gerados pelo mesmo modelo, porém, utilizando conjuntos fuzzy do tipo-1 e, o outro, gerado pelo método de Wang&Mendel. Métodos estatísticos de comparação usualmente aplicados em contextos semelhantes mostraram aumento significativo na taxa de classificação do classificador fuzzy intervalar do tipo-2 gerado pelo modelo em relação aos outros dois classificadores utilizados para comparação.
|
Page generated in 0.346 seconds