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
Anti-periodic solutions for state-dependent impulsive recurrent neural networks with time-varying and continuously distributed delays
Date
2017-11-01
Author
Sayli, Mustafa
YILMAZ, ENES
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
195
views
0
downloads
Cite This
In this paper, we address a new model of neural networks related to the impulsive phenomena which is called state-dependent impulsive recurrent neural networks with time-varying and continuously distributed delays. We investigate sufficient conditions on the existence and uniqueness of exponentially stable anti-periodic solution for these neural networks by employing method of coincide degree theory and an appropriate Lyapunov function. Moreover, we present an illustrative example to show the effectiveness and feasibility of the obtained theoretical results.
Subject Keywords
Management Science and Operations Research
,
General Decision Sciences
URI
https://hdl.handle.net/11511/64720
Journal
ANNALS OF OPERATIONS RESEARCH
DOI
https://doi.org/10.1007/s10479-016-2192-6
Collections
Department of Mathematics, Article
Suggestions
OpenMETU
Core
Mutual relevance of investor sentiment and finance by modeling coupled stochastic systems with MARS
Kalayci, Betul; Ozmen, Ayse; Weber, Gerhard Wilhelm (Springer Science and Business Media LLC, 2020-08-01)
Stochastic differential equations (SDEs) rapidly become one of the most well-known formats in which to express such diverse mathematical models under uncertainty such as financial models, neural systems, behavioral and neural responses, human reactions and behaviors. They belong to the main methods to describe randomness of a dynamical model today. In a financial system, different kinds of SDEs have been elaborated to model various financial assets. On the other hand, economists have conducted research on s...
Stochastic differential games for optimal investment problems in a Markov regime-switching jump-diffusion market
Savku, E.; Weber, Gerhard Wilhelm (Springer Science and Business Media LLC, 2020-08-01)
We apply dynamic programming principle to discuss two optimal investment problems by using zero-sum and nonzero-sum stochastic game approaches in a continuous-time Markov regime-switching environment within the frame work of behavioral finance. We represent different states of an economy and, consequently, investors' floating levels of psychological reactions by aD-state Markov chain. The first application is a zero-sum game between an investor and the market, and the second one formulates a nonzero-sum sto...
Formulation and solution of an optimal control problem for industrial project control
Schmidt, Klaus Verner (Springer Science and Business Media LLC, 2019-09-15)
n this paper, we consider the monitoring and control of industrial projects that are performed by executing different activities within a given time duration. Hereby, it is desired to apply project control to each activity in order to avoid unexpected deviations in the project cost, respecting that the amount and cost of project control needs to be limited. We model the general setting of industrial project control as an optimal control problem with the goal of maximizing the cost reduction (savings) when a...
Neural network calibrated stochastic processes: forecasting financial assets
Giebel, Stefan; Rainer, Martin (Springer Science and Business Media LLC, 2013-03-01)
If a given dynamical process contains an inherently unpredictable component, it may be modeled as a stochastic process. Typical examples from financial markets are the dynamics of prices (e.g. prices of stocks or commodities) or fundamental rates (exchange rates etc.). The unknown future value of the corresponding stochastic process is usually estimated as the expected value under a suitable measure, which may be determined from distribution of past (historical) values. The predictive power of this estimati...
An approximation for kanban controlled assembly systems
TOPAN, Engin; Avşar, Zeynep Müge (Springer Science and Business Media LLC, 2011-01-01)
An approximation is proposed to evaluate the steady-state performance of kanban controlled two-stage assembly systems. The development of the approximation is as follows. The considered continuous-time Markov chain is aggregated keeping the model exact, and this aggregate model is approximated replacing some state-dependent transition rates with constant rates. The approximate aggregate model is, then, decomposed into submodels and a product-form steady-state distribution is obtained for each submodel. Fina...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
M. Sayli and E. YILMAZ, “Anti-periodic solutions for state-dependent impulsive recurrent neural networks with time-varying and continuously distributed delays,”
ANNALS OF OPERATIONS RESEARCH
, pp. 159–185, 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/64720.