M
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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Journal ArticleDOI
Adaptive recurrent neural network with Lyapunov stability learning rules for robot dynamic terms identification
TL;DR: A recurrent neural network coupled with Kalman filter is proposed to identify dynamic terms of robotic manipulator, and disturbance rejection and robustness tests admit capability of the method for online dynamic identification in the presence of output and dynamic perturbation.
Proceedings ArticleDOI
Sliding mode control of Rotary Inverted Pendulm
TL;DR: Sliding mode control of rotary inverted pendulum is presented and based on this performance two sliding surfaces are designed, then system is controlled by proper definition of a lyapunov function.
Journal ArticleDOI
Breast Cancer Classification Based on Advanced Multi Dimensional Fuzzy Neural Network
TL;DR: Simulation results show the effectiveness of these methods even with less rules and parameters in performance result and maintain the accuracy of original fuzzy neural system and have high interpretability by human in diagnosis of breast cancer.
Journal ArticleDOI
Performance evaluation and design for variable threshold alarm systems through semi-Markov process.
TL;DR: Two types of variable thresholding are introduced and a novel approach for performance assessment of VTASs using Priority-AND gate and semi-Markov process is proposed, allowing the proposed approach to consider industrial measurements with non-Gaussian distributions.
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Generalized projective synchronization for chaotic systems via Gaussian Radial Basis Adaptive Backstepping Control
TL;DR: The proposed method allows us to arbitrarily adjust the desired scaling by controlling the slave system and it is not necessary to calculate the Lyapunov exponents and the eigen values of the Jacobian matrix, which makes it simple and convenient.