The Mamdani and TSK fuzzy models are fuzzy inference engines which have been most widely applied in real-world problems. Compared to the Mamdani approach, the TSK approach is more convenient when the crisp outputs are required. Common to both approaches, when a given observation does not overlap with any rule antecedent in the rule base (which usually termed as a sparse rule base), no rule can ...

A suitable interpolation method is essential to keep the noise level minimum along with the time-delay. In recent years, many different interpolation filters have been developed for instance H.264-6 tap filter, and AVS4 tap filter. The present work uses Adaptive Neuro-Fuzzy Inference System (ANFIS) technique to model and investigate the effects of a four-tap low-pass tap filter (Grid-adaptive f...

In this paper, we proposed another new form of type-2 fuzzy data points(T2FDPs) that is perfectly normal type-2 data points(PNT2FDPs). These kinds of brand-new data were defined by using the existing type-2 fuzzy set theory(T2FST) and type-2 fuzzy number(T2FN) concept since we dealt with the problem of defining complex uncertainty data. Along with this restructuring, we included the fuzzificati...

Prediction of sinusoidal signals with time-varying frequencies has been an important research topic in power electronics systems. To solve this problem, we propose a new fuzzy predictive filtering scheme, which is based on a Finite Impulse Response (FIR) filter bank. Fuzzy logic is introduced here to provide appropriate interpolation of individual filter outputs. Therefore, instead of regular ‘...

When a fuzzy system is presented with an incomplete (or sparse) rule base, interpolation (FRI) offers useful mechanism to infer conclusions for unmatched observations. However, most existing FRI methodologies are established Mamdani inference models, but not Takagi–Sugeno–Kang (TSK) ones. This article presents novel approach computing interpolated outcomes TSK using only small number of neighbo...

The role of linguistic context in the linguistic oriented fuzzy control is emphasized and methods for learning of the linguistic context are presented. The linguistic context of the independent variable is determined automatically whereas the linguistic context of the dependent variable is interpolated from known values .

László T. Kóczy* **, Domonkos Tikk* ** and Tamás D. Gedeon** * Budapest University of Technology and Economics, Budapest, Hungary ** Murdoch University, Perth, W.A., Australia