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A previously developed and analyzed deterministic model for the transmission dynamics of measles, which takes into account the possibility of vaccinated people also contracting the disease, has been developed for Turkey. The model consists of ...

Inverse problems aim to find the causes of outcoming features knowing the consequences of a model by calibrating the model’s parameters to fit data. In this paper, we present a method that solves simultaneously the inverse problem and the state ...

• Deterministic approach to calibrate state variables and parameters.
• Error in the initial condition is handled separately from the measurement noise.
• Epistemic uncertainty is incorporated in the misfit definition.
• We calibrate ...

An epidemic causes significant financial and economic losses in addition to having negative health effects that result in fatalities.Unemployment is one of the key macroeconomic challenges that governments around the world experience when an ...

In this paper, we propose an identification algorithm that utilizes the least squares principle based on the auxiliary model for parameter identification of discrete time-delay periodic linear systems. Initially, we introduce the period transfer ...

• This paper identifies discrete time-delay periodic linear systems with d-unit delays.
• We utilizes auxiliary model and recursive least squares for parameter identification.
• Two numerical examples demonstrate the proposed algorithm’s ...

We propose a novel framework for efficient parameter estimation in simulation models, formulated as an optimization problem that minimizes the discrepancy between physical system observations and simulation model outputs. Our framework, called ...

In this paper, we introduce a high-precision Hermite neural network solver which employs Hermite interpolation technique to construct high-order explicit approximation schemes for fractional derivatives. By automatically satisfying the initial ...

• A high-precision Hermite neural network solver is developed to simulate the time fractional Allen-Cahn equation.
• Improved accuracy and efficiency for 1D and 2D time fractional Allen-Cahn forward and parameter estimation problems.
• ...

This study suggests the pricing problems of options dependent on multiple assets, spread, basket, and quanto options when the asset dynamics are described by the uncertain fractional differential equation. The solutions of these option prices are ...

• Parameter estimation of uncertain fractional differential equations (UFDEs) by minimum cover method.
• Option pricing written on multiple assets when the asset price dynamics are described by UFDEs.
• Valuation of uncertain hypothesis ...

This paper addresses the complex challenge of parameter estimation in multi-component Linear Frequency Modulation (LFM) signals by introducing an innovative approach to high-resolution Fractional Fourier Transform (FrFT) parameter estimation, ...

Global navigation satellite systems reflectometry (GNSS-R) is a technique to extract information from reflecting surfaces by the reflected GNSS signals. GNSS-R has garnered increasing attention in the scientific literature due to its continuous ...

• Introduction of a consistent and realistic modeling approach for GNSS-R propagation scenarios, considering the two circular polarization components of the specularly reflected signalR, addressing the limitations of single-polarization ...

In this work, we present a compartmental dynamic model of tuberculosis infection among diabetic population of a certain demography and apply neural network algorithm to estimate parameters, forecast disease spread efficiently. The model is built ...

This study introduces a novel parameter estimation method based on Neural Ordinary Differential Equations (Neural ODE). The method addresses the challenges of limited data and noise interference in dynamic system modeling.By integrating neural ...

• A new method for dynamic system parameter estimation using neural ordinary differential equations (NODEs).
• Integrates unknown parameters directly into NODE training for end-to-end learning via backpropagation.
• Leverages physical ...

An adaptive beamforming method based on the polarimetric scattering matrix (PSM) of targets to suppress deceptive jamming located in the beam pattern main lobe is proposed in this study. For the netted radar system, the true/false mixed targets ...

To enhance the resolution of synchrosqueezing transform (SST) in non-stationary signal representation, an optimization synchrosqueezed fractional wavelet transform (SSFRWT) is proposed, which possesses rigorous mathematical principle and high ...

Given a linear regression model in discrete-time containing a vector of p constant uncertain parameters, this paper addresses the problem of designing an exponentially convergent parameter estimation algorithm, even when the regressor vector is ...

The primary objective of this study is to address the challenges associated with estimating parameters in mathematical epidemiology models, which are crucial for understanding the dynamics of infectious diseases within a population. The scope of ...

This study introduces a new approach for parameter optimization in the four-diode photovoltaic (PV) model, employing a Dynamic Fitness-Guided Particle Swarm Optimization (DFGPSO) algorithm and Enhanced Newton-Raphson (ENR) method. The new DFGPSO ...

• New optimization framework for four-diode PV model using DFGPSO with ENR method.
• DFGPSO eliminates local optima, enhances global search, and ensures quick, accurate convergence.
• ENR method boosts robustness by providing accurate ...

Biochemical networks are usually modeled by ordinary differential equations that describe the time evolution of the concentrations of the interacting (biochemical) species for specific initial concentrations and certain values of the interaction ...

• Biochemical networks can be approximated by discrete probabilistic models.
• Bayesian inference is used for parameter estimation.
• Probabilistic parameter estimation is made accurate and computationally efficient.
• Application to ...

The first-order binomial autoregressive (BAR(1)) model is the most frequently used tool to analyze the bounded count time series. The BAR(1) model is stationary and assumes process parameters to remain constant throughout the time period, which ...

The mixture of two 2-parameter Weibull distributions (MixW), as a specialized variant of the mixture of Weibull distributions, serves as an ideal model for heterogeneous data sets within the realms of reliability studies and survival analysis. A ...

CT perfusion (CTP) imaging is vital in treating acute ischemic stroke by identifying salvageable tissue and the infarcted core. CTP images allow quantitative estimation of CT perfusion parameters, which can provide information on the ...

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• We propose a novel neural network for CT perfusion parameter estimation in stroke.
• The network demonstrates superior accuracy and precision compared to SVD on the simulation dataset.
• In real-world patient dataset, we can rationally ...