A
Ardashir Mohammadzadeh
Researcher at University of Bonab
Publications - 130
Citations - 2092
Ardashir Mohammadzadeh is an academic researcher from University of Bonab. The author has contributed to research in topics: Computer science & Fuzzy logic. The author has an hindex of 17, co-authored 70 publications receiving 671 citations. Previous affiliations of Ardashir Mohammadzadeh include University of Tabriz & K.N.Toosi University of Technology.
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An Interval Type-3 Fuzzy System and a New Online Fractional-Order Learning Algorithm: Theory and Practice
TL;DR: It is shown that the proposed fuzzy system and associated learning algorithm result in better approximation performance in comparison with the other well-known approaches.
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A New Online Learned Interval Type-3 Fuzzy Control System for Solar Energy Management Systems
TL;DR: In this article, an interval type-3 fuzzy logic system (IT3-FLS) and an online learning approach are designed for power control and battery charge planing for photovoltaic (PV)/battery hybrid systems.
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Synchronization of uncertain fractional-order hyperchaotic systems by using a new self-evolving non-singleton type-2 fuzzy neural network and its application to secure communication
TL;DR: A robust control method for synchronization of the uncertain fractional-order hyperchaotic systems by using a new self-evolving non-singleton type-2 fuzzy neural network (SE-NT2FNN).
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Fixed-time synchronization analysis for discontinuous fuzzy inertial neural networks with parameter uncertainties
TL;DR: Using a new variable transformation and differential inclusions theory, a new framework is provided to deal with the inertial neural networks with fuzzy logics and discontinuous activation functions and some sufficient criteria are derived for achieving fixed-time synchronization.
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Deep learned recurrent type-3 fuzzy system: Application for renewable energy modeling/prediction
Yan Cao,Amir Raise,Ardashir Mohammadzadeh,Sakthivel Rathinasamy,Shahab S. Band,Amirhosein Mosavi +5 more
TL;DR: In this article, a deep learned recurrent type-3 (RT3) fuzzy logic system (FLS) with nonlinear consequent part is presented for renewable energy modeling and prediction. And the proposed method is applied for modeling both solar panels and wind turbines.