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Ali Khaki Sedigh
Researcher at K.N.Toosi University of Technology
Publications - 118
Citations - 1362
Ali Khaki Sedigh is an academic researcher from K.N.Toosi University of Technology. The author has contributed to research in topics: Control theory & Model predictive control. The author has an hindex of 18, co-authored 115 publications receiving 1192 citations.
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Journal ArticleDOI
Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods
TL;DR: It is shown that instability will not occur for the leaning rate and PSO factors in the presence of constraints and stable learning algorithms for two common methods are proposed based on Lyapunov stability theory and some constraints are obtained.
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Training ANFIS as an identifier with intelligent hybrid stable learning algorithm based on particle swarm optimization and extended Kalman filter
TL;DR: It is shown that applying PSO, a powerful optimizer, to optimally train the parameters of the membership function on the antecedent part of the fuzzy rules in ANFIS system is a stable approach which results in an identifier with the best trained model.
Journal ArticleDOI
Forecasting of short-term traffic-flow based on improved neurofuzzy models via emotional temporal difference learning algorithm
TL;DR: The research discussed the strong points of new method based on neurofuzzy and limbic system structure against classical and other intelligent methods and confirmed the significance of structural brain modeling beyond the classical artificial neural networks.
Journal ArticleDOI
Analytical design of fractional order PID controllers based on the fractional set-point weighted structure: Case study in twin rotor helicopter
TL;DR: In this paper, an analytical method for tuning the parameters of the set-point weighted fractional order PID (SWFOPID) controller is proposed, which is applicable to stable plants describable by a simple three-parameter fractional-order model.
Proceedings ArticleDOI
Novel Hybrid Learning Algorithms for Tuning ANFIS Parameters Using Adaptive Weighted PSO
TL;DR: The simulation results show that in comparison with current gradient based training, the novel training can have a comparable adaptation to complex plants and train less parameter than gradient base methods.