This paper presents a novel H tracking-based direct adaptive fuzzy controller (HDAFC) for a class of perturbed uncertain affine nonlinear systems involving external disturbances and measurement noise. A practical interval type-2 (IT2) fuzzy logic system (FLS) is introduced to approximate the ideal control law. To eliminate the tradeoff between H tracking performance and high gain at the control...

This paper presents a novel adaptive neuro-fuzzy inference system based on interval Gaussian type-2 fuzzy sets in the antecedent part and Gaussian type-1 fuzzy sets as coefficients of linear combination of input variables in the consequent part. The capability of the proposed ANFIS2 for function approximation and dynamical system identification is remarkable. The structure of ANFIS2 is very sim...

An adaptive neuro fuzzy inference system based on interval Gaussian type-2 fuzzy sets in the antecedent part and Gaussian type-1 fuzzy sets as coefficients of linear combination of input variables in the consequent part is presented in this paper. The capability of the proposed method (we named ANFIS2) for function approximation and dynamical system identification is remarkable. The structure o...

Article history: Received 15 November 2012 Received in revised form 3 January 2014 Accepted 1 February 2014 Available online 10 February 2014

Fuzziness is an inherent property of geographical phenomena and the processes data acquisition, processing, analysis often introduce uncertainty. Existing methods predominantly use fuzzy set (FS) theory to capture fuzziness as spatial objects. However, this approach has a conceptual confusion regarding fuzziness, uncertainty, vagueness, membership degree expressed using accurate values that ign...

When constructing the model of a word by collecting interval-valued data from a group of individuals, both interpersonal and intrapersonal uncertainties coexist. Similar to the interval type-2 fuzzy set (IT2 FS) used in the enhanced interval approach (EIA), the Cloud model characterized by only three parameters can manage both uncertainties. Thus, based on the Cloud model, this paper proposes a...

In this paper an adaptive neuro fuzzy inference system based on interval Gaussian type-2 fuzzy sets in the antecedent part and Gaussian type-1 fuzzy sets as coefficients of linear combination of input variables in the consequent part is presented. The capability of the proposed method (we named ANFIS2) to function approximation and dynamical system identification is shown. The ANFIS2 structure ...

Using Multi-Criteria Decision-Making (MCDM) to solve complicated decisions often includes uncertainty, which could be tackled by utilizing the fuzzy sets theory. Type-2 fuzzy sets consider more uncertainty than type-1 fuzzy sets. These fuzzy sets provide more degrees of freedom to illustrate the uncertainty and fuzziness in real-world production projects. In this paper, a new multi-criteria ana...