Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Optimal multi-objective control method for discrete genetic regulatory networks
Date
2006-10-18
Author
Abul, Osman
Alhajj, Reda
Polat, Faruk
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
146
views
0
downloads
Cite This
In this paper we study the control problem and note that it is multi-objective by nature, and thus we develop an optimal multi-objective approach. Our approach includes formalizing components and identifying dimensions, resulting in few cases for concrete problem formulation. For a selected case, namely the finite control case, a single-objective from the literature and our multi-objective solutions are presented. It is demonstrated that the multi-objective solution avoids drawbacks of the single-objective solution, particularly the need for defining single objective out of many
URI
https://hdl.handle.net/11511/38818
DOI
https://doi.org/10.1109/bibe.2006.253345
Collections
Department of Computer Engineering, Conference / Seminar
Suggestions
OpenMETU
Core
Optimal Limit Order Book Trading Strategies with Stochastic Volatility in the Underlying Asset
Aydoğan, Burcu; Uğur, Ömür; Aksoy, Ümit (2022-1-01)
In quantitative finance, there have been numerous new aspects and developments related with the stochastic control and optimization problems which handle the controlled variables of performing the behavior of a dynamical system to achieve certain objectives. In this paper, we address the optimal trading strategies via price impact models using Heston stochastic volatility framework including jump processes either in price or in volatility of the price dynamics with the aim of maximizing expected return of t...
Distributed optimal control of time-dependent diffusion-convection-reaction equations using space-time discretization
Seymen, Z. Kanar; Yücel, Hamdullah; Karasözen, Bülent (2014-05-01)
We apply two different strategies for solving unsteady distributed optimal control problems governed by diffusion-convection-reaction equations. In the first approach, the optimality system is transformed into a biharmonic equation in the space time domain. The system is then discretized in space and time simultaneously and solved by an equation-based finite element package, i.e., COMSOL Multiphysics. The second approach is a classical gradient-based optimization method to solve the state and adjoint equati...
Optimal Policy Synthesis from A Sequence of Goal Sets with An Application to Electric Distribution System Restoration
Isik, Ilker; Arpalı, Onur Yigit; Aydın Göl, Ebru (2021-01-01)
Motivated by the post-disaster distribution system restoration problem, in this paper, we study the problem of synthesizing the optimal policy for a Markov Decision Process (MDP) from a sequence of goal sets. For each goal set, our aim is to both maximize the probability to reach and minimize the expected time to reach the goal set. The order of the goal sets represents their priority. In particular, our aim is to generate a policy that is optimal with respect to the first goal set, and it is optimal with r...
Optimal multiple hypothesis testing with an application in side lobe blanker design and invariance applications in detection and synchronization
Coşkun, Osman; Candan, Çağatay; Department of Electrical and Electronics Engineering (2017)
This thesis aims to study two problems, namely optimal hypothesis testing in the sense of Neyman-Pearson in the presence of multiple hypotheses and optimal hypothesis testing in the presence of non-random unknown parameters (nuisance parameters). Both problems occur frequently in different applications and their optimal solution involves some fine details. In the first part of the thesis, the multiple hypothesis testing problem is examined and the results are applied on the problem of radar sidelobe blanker...
Uncertainty quantification of parameters in local volatility model via frequentist, bayesian and stochastic galerkin methods
Animoku, Abdulwahab; Uğur, Ömür; Department of Financial Mathematics (2018)
In this thesis, we investigate and implement advanced methods to quantify uncertain parameter(s) in Dupire local volatility equation. The advanced methods investigated are Bayesian and stochastic Galerkin methods. These advanced techniques implore different ideas in estimating the unknown parameters in PDEs. The Bayesian approach assumes the parameter is a random variable to be sampled from its posterior distribution. The posterior distribution of the parameter is constructed via “Bayes theorem of inverse p...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
O. Abul, R. Alhajj, and F. Polat, “Optimal multi-objective control method for discrete genetic regulatory networks,” 2006, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/38818.