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Asef Zare

Researcher at Islamic Azad University

Publications -  8
Citations -  129

Asef Zare is an academic researcher from Islamic Azad University. The author has contributed to research in topics: Fuzzy logic & Variable structure control. The author has an hindex of 4, co-authored 8 publications receiving 116 citations.

Papers
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Journal ArticleDOI

Design of Optimal Fractional-Order PID Controllers Using Particle Swarm Optimization Algorithm for Automatic Voltage Regulator (AVR) System

TL;DR: Simulations and comparisons with other FOPID/PID controllers illustrate that the proposed PSO-FOPID controller can provide good control performance with respect to reference input and also improve the system robustness withrespect to model uncertainties.
Proceedings ArticleDOI

Application of optimized Type 2 fuzzy time series to forecast Taiwan stock index

TL;DR: In this paper, Taiwan stock index is forecasted by use of optimized type 2 fuzzy time series concept that is closer to reality than type1 model.
Journal ArticleDOI

Stabilization of multi-input hybrid fractional-order systems with state delay

TL;DR: In this paper, the stabilization of a particular class of multi-input linear systems of fractional order differential inclusions with state delay using variable structure control is considered and the concepts related to sliding control stabilization of differential inclusion systems with integer order are extended.
Journal ArticleDOI

Variable structure control of linear time invariant fractional order systems using a finite number of state feedback law

TL;DR: In this article, an approach based on the variable structure control is proposed for stabilization of linear time invariant fractional order systems (LTI-FOS) using a finite number of available state feedback controls, none of which is capable of stabilizing the FOS by itself.

Automatic road extraction based on neuro-fuzzy algorithm

TL;DR: For better detection and extraction of roads, a fuzzy network is trained through a neural network and shows that, the neurofuzzy algorithm gives much better performance than the fuzzy algorithm.