Loading...
Search for:
hasanzade--mehrnoosh
0.061 seconds
An event-triggered robust data-driven predictive control with transient response improvement
, Article 2023 31st International Conference on Electrical Engineering, ICEE 2023 ; 2023 , Pages 488-491 ; 979-835031256-0 (ISBN) ; Haeri, M ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2023
Abstract
We develop a data-driven model predictive control (MPC) design procedure to control unknown linear time-invariant systems. This algorithm only requires measured input-output data to drive the system to the reference signal. We add filters on desired inputs and outputs in the cost function to improve the transient response. Moreover, the Hankel matrices are updated online based on a multi-step event-triggered MPC scheme to deal with the uncertainties. This also reduces the computational cost and balances it with the closed-loop performance. Simulation results illustrate effectiveness of the proposed approach. © 2023 IEEE
Design of a Model Predictive Controller Based on Data-Driven Approach
, M.Sc. Thesis Sharif University of Technology ; Haeri, Mohammad (Supervisor)
Abstract
In this dissertation, a data-driven model predictive control (MPC) algorithm has been developed to control unknown linear time invariant systems. This algorithm only needs past measured input-output data of the system and an upper bound on the order of the system to derive the system output to a desired value. To improve the transient response of the system, we define two filters on the performance and the effort terms of cost function of the optimization problem. Moreover, the Hankel matrices used in the optimization problem are updated online based on a multi-step event-triggered MPC scheme to deal with uncertainties and disturbances. With this method, the computational load of this...
Bias of a Value-at-Risk Estimator
, M.Sc. Thesis Sharif University of Technology ; Keshavarz Hadad, Golamreza (Supervisor)
Abstract
The recent researches show that Value at Risk estimations are biased and is calculated conservatively. Bao and Olah (2004) proved that the bias of an ARCH(1) model for VaR can be formulated in to two parts: bias due to return Misspecification ( ) and bias due to estimation error ( ). Using a GARCH(1,1) and quasi maximum likelihood estimation method, this research intends to find an analytical framework for the two source of biases. We generate returns from Normal and t-student distributions, then estimate the GARCH(1,1) under Normal and t-student assumptions. Our findings reveal that equals to zero for the Normal likelihood function, but . Also, and are not zero for the t-student...
Thermodynamic model for prediction of performance and emission characteristics of SI engine fuelled by gasoline and natural gas with experimental verification
, Article Journal of Mechanical Science and Technology ; Volume 26, Issue 7 , July , 2012 , Pages 2213-2225 ; 1738494X (ISSN) ; Asghar, H. A ; Asghar, M. A ; Sharif University of Technology
2012
Abstract
In this study, a thermodynamic cycle simulation of a conventional four-stroke SI engine has been carried out to predict the engine performance and emissions. The first law of thermodynamics has been applied to determine in-cylinder temperature and pressure as a function of crank angle. The Newton-Raphson method was used for the numerical solution of the equations. The non-differential form of equations resulted in the simplicity and ease of the solution to predict the engine performance. Two-zone model for the combustion process simulation has been used and the mass burning rate was predicted by simulating spherical propagation of the flame front. Also, temperature dependence of specific...
Designing a new algorithm for the fuzzy shortest path problem in a network
, Article 37th International Conference on Computers and Industrial Engineering 2007, Alexandria, 20 October 2007 through 23 October 2007 ; Volume 1 , 2007 , Pages 556-563 ; 9781627486811 (ISBN) ; Tajdin, A ; Nourifar, R ; Hasanzade, R ; Mahdavi Amiri, N ; Sharif University of Technology
2007
Abstract
The shortest path problem is a classical and important network optimization problem appearing in many applications. We discuss the shortest path problem from a specified vertex to every other vertex on a network with imprecise arc lengths as fuzzy numbers. Using an order relation between fuzzy numbers, we propose a new algorithm to deal with the fuzzy shortest path problem. The algorithm is composed of a fuzzy shortest path length procedure and a similarity measure. The fuzzy shortest length method is proposed to find the fuzzy shortest length, and the fuzzy similarity measure is utilized to get the shortest path. Two illustrative examples are worked out to demonstrate the proposed algorithm...
Sense Tagging a Persian Corpus
, M.Sc. Thesis Sharif University of Technology ; Khosravizade, Parvaneh (Supervisor) ; Shams Fard, Mehrnoosh (Co-Advisor)
Abstract
The main focus of this research is to resolve the semantic ambiguity in Persian. In this study, a semi-supervised machine learning method is proposed to choose the most proper meaning of a target word in the context. Several statistical methods are compared, and the most accurate one is chosen for developing a sense tagger. An initial seed data is built by searching collocation lists for each sense. After developing the sense tagger and initial seed set, a bootstrapping method is used to sense tag all occurences of a target word in corpus with 90% accuracy