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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Recurrent Interval Type-2 Fuzzy Wavelet Neural Network with Stable Learning Algorithm: Application to Model-Based Predictive Control
TL;DR: A novel identification model based on recurrent interval type-2 fuzzy wavelet neural network (RIT2FWNN) with new learning algorithm to prove learning dynamics and asymptotic stability of the network by using an appropriate Lyapunov function.
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Optimal control of non-smooth fractional-order systems based on extended Caputo derivative
TL;DR: In this paper, a sub-optimal controller is proposed for a class of non-smooth fractional-order systems, where a new generalized Bernstein expansion is obtained for the original nonsmooth function.
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Optimization of Interval Type-2 Fuzzy Logic System Using Grasshopper Optimization Algorithm
Saima Towhida Hassan,Mojtaba Ahmadieh Khanesar,Nazar Kalaf Hussein,Samir Brahim Belhaouari,Usman Amjad,Wali Khan Mashwani +5 more
TL;DR: Analysis of the performance, on the same data-sets, reveals that the proposed GOAIT2FELM could be a better approach for improving the accuracy of the IT2-FLS as compared to other variants of the optimized IT2 -FLS.
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Optimal synchronization of non-smooth fractional order chaotic systems with uncertainty based on extension of a numerical approach in fractional optimal control problems
TL;DR: The results show that using the suggested control mechanism, the synchronization is fast and robust against parametric uncertainty in Chua slave system.
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Comparative analysis of three approaches of antecedent part generation for an IT2 TSK FLS
TL;DR: Heuristic optimization approaches such as genetic algorithm and artificial bee colony are used to optimize the parameters of the antecedent part of interval type-2 fuzzy logic systems to support the generation of optimal parameters.