Static criteria for fuzzy systems quality evaluation
In consensus research, it is necessary to find criteria to assign confidence factors to the knowledge-based systems involved in a consensus algorithm. Those factors must reflect the confidence that we can have on each system's assertions. A whole class of such criteria are static ones (we call them quality criteria), that is, criteria based on the structure of the systems more than on any performance measure.
In the present work, we propose, justify and formalize three static quality evaluation criteria for fuzzy systems: Completeness, Redundancy and Consistence. They are based on similar ones existing in classical logic, but they are generalized to the fuzzy domain. This is mainly done by making use of the subsethood theorem of Kosko's Set-as-Points framework, a very convenient way to assign geometric meaning to fuzzy sets.
Unable to display preview. Download preview PDF.
- 1.Kosko, B.: Neural Networks and Fuzzy Systems. Prentice-Hall International 1992.Google Scholar
- 2.Kinkielele, D.: On the Consistency of Fuzzy Knowledge Bases. Proceedings of the European Symposium on the Validation and Verification of Knowledge-Based Systems. pp 247–261 Palma de Mallorca March 24–26 1993.Google Scholar
- 3.Zadeh, L.A: Fuzzy Sets Information and Control, 8, 338–353Google Scholar
- 4.Sala, A.: The Inference Error Minimization Approach to Fuzzy Inference and Knowledge Analysis, Symposium on Qualitative System Modelling, Qualitative Fault Diagnosis and Fuzzy Logic Control, pp. 309–315, Budapest, April 17–21, 1996.Google Scholar
- 5.Torra,V, Cortes, U.: Towards an Automatic Consensus Generation Tool IEEE transactionson Systems, Man, and Cybernetics. vol. 26, n. 5. May 1995.Google Scholar
- 6.Oller, A. et al.: Us d'un parametre de qualitat per millorar l'eficiencia d'un algorisme de consensus. Simulacio amb MATLAB. Research Report, Institut d'Informatica i Aplicacions. Universitat de Girona. 1998Google Scholar