A novel learning methodology based on a hybrid mechanism for training interval singleton type-2 Takagi-SugenoKang fuzzy logic systems uses recursive orthogonal least-squares to tune the type-1 consequent parameters and the steepest descent method to tune the interval type-2 antecedent parameters. The proposed hybrid-learning algorithm changes the interval type-2 model parameters adaptively to m...

Type-2 fuzzy logic to make up for the lack of a type of fuzzy logic in dealing with uncertainty, object contains uncertainty is strong; the application of type-2 fuzzy logic advantage is more obvious. In this paper, type-2s of fuzzy logic for power load time series forecasting, good results were obtained. According to the power load has strong randomness it is difficult to accurately forecast p...

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...

This paper presents the theory and design of interval type-2 fuzzy logic systems (FLSs) because of computational complexity of using general type-2 fuzzy set (T2FS) in type-2 fuzzy system. We propose an efficient and simplified method to compute the input and antecedent operations for interval type-2 FLSs; one that is based on a general inference formula for them. We introduce the concept of up...

A method for response integration in modular neural networks with type-2 fuzzy logic for biometric systems p. 5 Evolving type-2 fuzzy logic controllers for autonomous mobile robots p. 16 Adaptive type-2 fuzzy logic for intelligent home environment p. 26 Interval type-1 non-singleton type-2 TSK fuzzy logic systems using the hybrid training method RLS-BP p. 36 An efficient computational method to...

This paper presents a novel approach to identify the prediction interval associated with data using type-2 fuzzy logic systems support vector regression. For such purpose, constrained quadratic objective function is defined which then solved well-established programming approaches. Not only does output of system replicates measured value, but also it provides lower bound and upper for values. I...

Interval type-2 fuzzy logic systems have a wide range of applications due to their abilities to handle uncertainties compared to their type-1 counterparts. This paper presents an approach for developing closed-form mathematical representations for interval type-2 fuzzy logic systems. Having such a representation can be useful in real-time implementation of the system and in its mathematical ana...

This paper presents the application of interval type-2 fuzzy inference systems (IT2FIS) in short term load forecasting (STLF) on special days. This is a continuation work of application interval type-2 fuzzy systems (IT2FSs) using Karnik Mendel algorithm. Special days here mean local Balinese holidays such as national and local culture-based public holidays, consecutive holidays, and days prece...

This paper presents research on applications of fuzzy logic and higher-order fuzzy logic systems to control filters reducing air pollution [1]. The filters use Selective Catalytic Reduction (SCR) method and, as for now, this process is controlled manually by a human expert. The goal of the research is to control an SCR system responsible for emission of nitrogen oxide (NO) and nitrogen dioxide ...