M
Mojtaba Ahmadieh Khanesar
Researcher at University of Nottingham
Publications - 103
Citations - 2002
Mojtaba Ahmadieh Khanesar is an academic researcher from University of Nottingham. The author has contributed to research in topics: Fuzzy logic & Fuzzy control system. The author has an hindex of 23, co-authored 96 publications receiving 1695 citations. Previous affiliations of Mojtaba Ahmadieh Khanesar include K.N.Toosi University of Technology & Semnan University.
Papers
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Proceedings ArticleDOI
A novel binary particle swarm optimization
TL;DR: This algorithm is shown to be a better interpretation of continuous PSO into discrete PSO than the older versions and a number of benchmark optimization problems are solved using this concept and quite satisfactory results are obtained.
Journal ArticleDOI
Adaptive Indirect Fuzzy Sliding Mode Controller for Networked Control Systems Subject to Time-Varying Network-Induced Time Delay
TL;DR: Simulation results show that the proposed fuzzy sliding mode controller is capable of controlling nonlinear dynamical systems over a network, which is subject to bounded external disturbances, time-varying network-induced delays, and packet losses with adequate performance.
Journal ArticleDOI
Extended Kalman Filter Based Learning Algorithm for Type-2 Fuzzy Logic Systems and Its Experimental Evaluation
TL;DR: The simulation results show that the proposed novel type-2 fuzzy membership function with the extended Kalman filter has noise rejection property, which is faster and more efficient than the particle swarm optimization method.
Journal ArticleDOI
Analysis of the Noise Reduction Property of Type-2 Fuzzy Logic Systems Using a Novel Type-2 Membership Function
TL;DR: The proposed type-2 fuzzy neuro structure is tested on different input-output data sets, and it is shown that the T2FLS with the proposed novel membership function has better noise reduction property when compared to the type-1 counterparts.
Journal ArticleDOI
Identification of Nonlinear Dynamic Systems Using Type-2 Fuzzy Neural Networks—A Novel Learning Algorithm and a Comparative Study
TL;DR: The developed algorithm applies fully-sliding-mode parameter update rules for both the premise and consequent parts of type-2 FNNs, and it has been realized and shown that the proposed algorithm has faster convergence speed than the existing methods such as gradient-based and swarm-intelligence-based methods.