This article discusses a rule-based fuzzy model for the identi cation of nonlinear MISO (multiple input, single output) systems. The dis cussed method of fuzzy modeling consists of two parts: structure modeling, i.e. determing the num ber of rules and input variables involved respec tively, and parameter optimization, i.e. optimizing the location and form of the curves which describe the fuz...

This article discusses a rule-based fuzzy model for the identiication of nonlinear MISO (multiple input, single output) systems. The diss cussed method of fuzzy modeling consists of two parts: structure modeling, i.e. determing the numm ber of rules and input variables involved respecc tively, and parameter optimization, i.e. optimizing the location and form of the curves which describe the fuz...

We propose Tree Sequence Kernel (TSK), which implicitly exhausts the structure features of a sequence of subtrees embedded in the phrasal parse tree. By incorporating the capability of sequence kernel, TSK enriches tree kernel with tree sequence features so that it may provide additional useful patterns for machine learning applications. Two approaches of penalizing the substructures are propos...

This work is focused on creating fuzzy granular classification models based on general type-2 fuzzy logic systems when consequents are represented by interval type-2 TSK linear functions. Due to the complexity of general type-2 TSK fuzzy logic systems, a hybrid learning approach is proposed, where the principle of justifiable granularity is heuristically used to define an amount of uncertainty ...

The Takagi Sugeno Kang (TSK) fuzzy approach is popular since its output either a constant or function. Parameter identification and structure are the two key requirements for building TSK system. input utilized in can have an impact on number of rules produced such way that employing more data dimensions typically results rules, which causes rule complexity. This issue be solved by dimension re...

The Takagi–Sugeno–Kang (TSK) type of fuzzy models has attracted a great attention of the fuzzy modeling community due to their good performance in various applications. Various approaches for modeling TSK fuzzy rules have been proposed in the literature. Most of them define their fuzzy subspaces based on the idea of training data being close enough instead of having similar functions. Besides, ...

Rectal temperature (Tre) is often used to adjust measurements of blood gases, but these adjusted measurements may not approximate temperatures during intense exercise at main sites of gas exchange: muscle and lung. To evaluate differences in blood gases between sites, temperatures (T) were measured with thermocouples in the rectum (re), in mixed venous blood (v), in gluteal muscle (mu), and on ...

OBJECTIVE To investigate the validity and reliability of the Tampa Scale for Kinesiophobia Heart (TSK-SV Heart), a brief questionnaire to detect kinesiophobia (fear of movement) in patients with coronary artery disease. DESIGN Methodological research (cross-sectional study). SUBJECTS A total of 332 patients, mean age 65 years (standard deviation 9.1) diagnosed with coronary artery disease a...

This article presents a new learning methodology based on a hybrid algorithm for interval type-2 TSK fuzzy logic systems (FLS). Using input-output data pairs during the forward pass of the training process, the interval type-2 TSK FLS output is calculated and the consequent parameters are estimated by recursive least-squares (RLS) method. In the backward pass, the error propagates backward, and...

The purpose of this study is to analyze the relationship between lower limbs skin temperature (Tsk) recorded by infrared thermography and creatine kinase (CK) response following an official soccer game. A professional soccer player, from the first division Brazilian League, defender, 27 years and 183 cm of height was analyzed. Measures of Tsk with a thermal camera and blood CK concentrations we...