A Self-Organized Fuzzy Neural Network Approach for Rule Generation of Fuzzy Logic Systems
This paper shows an algorithm for creating fuzzy logic systems from data by synchronizing its fuzzy sets and rules using a novel neuro fuzzy approach to generate rules and fuzzy sets from analyzing input data. A volatile time series example is solved and analyzed using the residuals of the model.
KeywordsFuzzy Logic System Gaussian Membership Function Neuro Fuzzy Approach Neural Fuzzy System Linear Stochastic Process
Unable to display preview. Download preview PDF.
- 2.Mendel, J.: Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Prentice Hall (2000)Google Scholar
- 3.Klir, G.J., Folger, T.A.: Fuzzy Sets, Uncertainty and Information. Prentice Hall (1992)Google Scholar
- 5.Law, A., Kelton, D.: Simulation System and Analysis. Mc Graw Hill International (2000)Google Scholar
- 6.Wang, L.X., Mendel, J.M.: Back-propagation fuzzy system as nonlinear dynamic system identifiers. In: Proceedings of the FUZZ-IEEE, vol. 8, pp. 1409–1418. IEEE (1992)Google Scholar