High-order interval type-2 Takagi-Sugeno-Kang fuzzy logic system and its application in acoustic emission signal modeling in turning process
- First Online:
- 338 Downloads
Type-2 fuzzy logic systems (FLSs) are gaining in popularity because of their capacity to handle rule uncertainties in a more complete way. Moreover, higher-order interval type-2 (IT2) FLS can reduce drastically the number of rules needed to perform the approximation and improve transparency and interpretation in many high-dimensional systems. This paper presents architecture and inference engine of generalized IT2 Takagi-Sugeno-Kang (TSK) FLS and the design method of higher-order IT2 FLS. An experimental acoustic emission (AE) signal modeling using a second-order IT2 TSK FLS in turning process is given to demonstrate the differences between the first-order and second-order IT2 FLSs and the advantage and efficiency of high-order IT2 FLS. The estimation of uncertainty of AE could be of great value to a decision maker and used to investigate tool wear condition during the machining process.
KeywordType-2 fuzzy logic system High order Fuzzy modeling Acoustic emission
Unable to display preview. Download preview PDF.
- 6.Dubais D, Prade H (1980) Fuzzy sets and systems: theory and applications. Academic, New YorkGoogle Scholar
- 12.Mendel JM (2001) Uncertain rule-based fuzzy logic systems—introduction on new directions. Prentice hall PTR, Upper Saddle RiverGoogle Scholar
- 13.Liang Q, Mendel JM (1999) An introduction to type-2 TSK fuzzy logic systems. In: 1999 IEEE International Fuzzy Systems Conference Processing, Seoul, KoreaGoogle Scholar
- 15.Bellman R (1961) Adaptive control processes: a guide tour. Princeton University Press, PrincetonGoogle Scholar
- 16.Ren Q, Baron L, Balazinski M (2008) High order type-2 fuzzy logic system. In: The 27th North American Fuzzy Information Processing Society Annual Conference, New York, United StatesGoogle Scholar
- 17.Demirli K, Muthukumaran P (2000) Higher order fuzzy system identification using subtractive clustering. J Intell Fuzzy Syst 9:129–158Google Scholar
- 19.Ren Q, Baron L, Balazinski M (2006) Type-2 Takagi-Sugeno-Kang fuzzy logic modelling using subtractive clustering. In: The 25th North American Fuzzy Information Processing Society Annual Conference, Montreal, CanadaGoogle Scholar
- 21.Ren Q, Baron L, Balazinski M (2011) Fuzzy identification of cutting acoustic emission with extended subtractive cluster analysis. Nonlinear Dynam. doi:10.1007/s11071-011-0173-5
- 22.Ren Q, Baron L, Balazinski M (2009) Application of type-2 fuzzy estimation on uncertainty in machining: an approach on acoustic emission during turning process. In: The 28th North American Fuzzy Information Processing Society Annual Conference, Cincinnati, Ohio, USAGoogle Scholar
- 23.Ren Q, Baron L, Balazinski M (2011) Type-2 fuzzy modeling for acoustic emission signal in precision manufacturing. Model Simulat Eng (696947) doi:10.1155/2011/696947