Fractional-order Hammerstein state-space modeling of nonlinear dynamic systems from input-output measurements.

This paper introduces a continuous-time fractional-order Hammerstein state-space model with a systematic identification algorithm for modeling nonlinear dynamic systems. The proposed model consists of a radial-basis function neural network followed by a fractional-order system. The proposed identification scheme is accomplished in two stages.

Estimation of the Cost of Smoking-Attributable Diseases (Five Selected Diseases): A Case in Kerman City, Iran, 2014.

Background: Cigarette seems to be the least valuable of addictive drugs. It is easily accessible to the public, and its harmful personal and social effects have attracted less attention. Therefore, the present research was carried out with the aim of presenting cost estimations of smoking-related diseases in smokers who aged 35 or higher in Kerman City, Iran, in 2014.

On observer-based controller design for Sugeno systems with unmeasurable premise variables.

This paper considers the design of observer-based controller for a class of continuous-time nonlinear systems presented by Takagi-Sugeno (T-S) model with unmeasurable premise variables. This T-S structure can represent a larger class of nonlinear systems as compared to the measurable premise variable case but its analysis is more complicated. To reduce the design complexity, a common output model for subsystems is employed by the use of local nonlinear rules.