Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
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
Parameter Estimations for the Modified Izhikevich Neuron Model with Optimization Methods
Date
2017-06-02
Author
Korkmaz, Nimet
Kılıç, Recai
Kalınlı, Adem
Öztürk, Ismail
Metadata
Show full item record
Item Usage Stats
67
views
0
downloads
Cite This
URI
https://hdl.handle.net/11511/87472
Collections
Unverified, Conference / Seminar
Suggestions
OpenMETU
Core
Parameter estimation in generalized partial linear models with Tikhanov regularization
Kayhan, Belgin; Karasözen, Bülent; Department of Scientific Computing (2010)
Regression analysis refers to techniques for modeling and analyzing several variables in statistical learning. There are various types of regression models. In our study, we analyzed Generalized Partial Linear Models (GPLMs), which decomposes input variables into two sets, and additively combines classical linear models with nonlinear model part. By separating linear models from nonlinear ones, an inverse problem method Tikhonov regularization was applied for the nonlinear submodels separately, within the e...
Parameter estimation in generalized partial linear models with conic quadratic programming
Çelik, Gül; Weber, Gerhard Wilhelm; Department of Scientific Computing (2010)
In statistics, regression analysis is a technique, used to understand and model the relationship between a dependent variable and one or more independent variables. Multiple Adaptive Regression Spline (MARS) is a form of regression analysis. It is a non-parametric regression technique and can be seen as an extension of linear models that automatically models non-linearities and interactions. MARS is very important in both classification and regression, with an increasing number of applications in many areas...
Parameter estimation in merton jump diffusion model
Özdemir, Tuğcan Adem; Vardar Acar, Ceren; Department of Statistics (2019)
Over the years, jump diffusion models become more and more important. They areused for many purposes in several branches such as economics, biology, chemistry,physics, and social sciences. The reason for prevalent usage of these jump modelsis that they capture stochastic movements and they are sensitive to jump points. It ispossible to measure sudden decreases/increases caused by some reasons such as wars,natural disasters, market crashes or some dramatic news, by jump diffusion models.Recently, US Dolla...
Parameter Estimation For Actuarial Sciences And Financial Mathematics İn Stochastic Differential Equations By Additive And Nonlinear Models And Continuous Optimization
Weber, Gerhard Wilhelm(2009-12-31)
Bu proje gerçek hayat finans sektöründe stokastik diferansiyel eşitliklerin finansal matematik uygulamaları gözönüne alınarak sunulmuştur. Finans sektör verileri kullanılarak sokastik yaklaşım modelleri kurmak çok zor bir uygulamadır. Bu modeller fiyat süreçleri, faiz oranları ve değişkenlikleri ve çeşitli türevleri içerebilir. Projedeki amacımız matematik biliminin modern yöntemlerini kullanarak, özellikle süreklilik ve ters problem teorileriyle optimal modelleme yapmaktır. Bu şekilde istatistiğin modeld...
Parameter sensitivity analysis of a nonlinear least-squares optimization-based anelastic full waveform inversion method
Askan Gündoğan, Ayşegül; Bielak, Jacobo; Ghattas, Omar (Elsevier BV, 2010-07-01)
In a recent article, we described a seismic inversion method for determining the crustal velocity and attenuation of basins in earthquake-prone regions. We formulated the problem as a constrained nonlinear least-squares optimization problem in which the constraints are the equations that describe the forward wave propagation. Here, we conduct a parametric study to investigate the influence of parameters such as the form of the regularization function, receiver density, preconditioning, noise level of the da...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
N. Korkmaz, R. Kılıç, A. Kalınlı, and I. Öztürk, “Parameter Estimations for the Modified Izhikevich Neuron Model with Optimization Methods,” 2017, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/87472.