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Mahdi Aliyari Shoorehdeli

Researcher at K.N.Toosi University of Technology

Publications -  169
Citations -  2173

Mahdi Aliyari Shoorehdeli is an academic researcher from K.N.Toosi University of Technology. The author has contributed to research in topics: Fuzzy control system & Control theory. The author has an hindex of 20, co-authored 157 publications receiving 1812 citations. Previous affiliations of Mahdi Aliyari Shoorehdeli include Islamic Azad University, Science and Research Branch, Tehran & Islamic Azad University.

Papers
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Adaptive variable structure hierarchical fuzzy control for a class of high-order nonlinear dynamic systems

TL;DR: A novel adaptive hierarchical fuzzy control system based on the variable structure control is developed for a class of SISO canonical nonlinear systems in the presence of bounded disturbances to investigate the stability of the nonlinear dynamic systems.
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Hybrid-controlled neurofuzzy networks analysis resulting in genetic regulatory networks reconstruction.

TL;DR: It is demonstrated that the proposed algorithm not only selects the patterns of the time series gene expression data accurately, but also provides models with better reconstruction accuracy when compared with four published algorithms: DBNs, VBEM, time delay ARACNE, and PF subjected to LASSO.
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Anti-swing control of a double-pendulum-type overhead crane using parallel distributed fuzzy LQR controller

TL;DR: This paper proposes a hybrid controller that includes both position regulation and anti-swing control and the validity of the proposed control algorithm is illustrated and it is compared with a similar method parallel distributed fuzzy controller.
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An interdisciplinary overview and intelligent control of human prosthetic eye movements system for the emotional support by a huggable pet-type robot from a biomechatronical viewpoint

TL;DR: A distinguished survey of existing literature on the intelligent control of the human eye movements system applied in a huggable pet-type robot as a biomechatronic system and the intelligent controls applied are emulated from the neural controls in biological system.
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Stability of Feedback Error Learning for Linear Systems

TL;DR: A simple single-layer neural network is assumed as an adaptive linear combiner and stability techniques are applied to derive the same adaptation law as feedback error learning rule for a linear representation of dynamic system with unknown parameters.