Fredrick Asenso Wireko - Profile on Academia.edu (original) (raw)

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Papers by Fredrick Asenso Wireko

Non-optimal and optimal fractional control analysis of measles using real data

Informatics in medicine unlocked, Jul 1, 2024

Modelling the dynamics of Ebola disease transmission with optimal control analysis

Modeling earth systems and environment, May 23, 2024

A fractional order Ebola transmission model for dogs and humans

Scientific African, May 1, 2024

In this paper, it is shown that discrete equations with Hilb ert matrix operator, circulant matri... more In this paper, it is shown that discrete equations with Hilb ert matrix operator, circulant matrix operator, conference matrix operator, banded matrix operator, and sparse matrix operator are ill-posed in the sense of Hadamard. These ill-posed problems cannot be regularized by Gauss Least Square Method (GLSM), QR Factorization Method (QRFM), Cholesky Decomposition Method (CDM) and Singular Value Decomposition (SVDM). To overcome the limitations of these methods of regularization, an Eigenspace Spectral Regularization Method (ESRM) is introduced which solves ill-p os ed discrete equations with Hilb ert matrix operator, circulant matrix operator, conference matrix operator, banded matrix operator, and sparse matrix operator. Unlike GLSM, QRFM, CDM, and SVDM, the ESRM regularize such a system. In addition, the ESRM has a unique property, the norm of the eigenspace spectral matrix operator κ (K) = ||K − 1K|| = 1. Thus, the condition number of ESRM is bounded by unity unlike the other re...

Modelling the transmission behavior of Measles disease considering contaminated environment through a fractal-fractional Mittag-Leffler kernel

Physica scripta, May 29, 2024

Optimal control dynamics of Gonorrhea in a structured population

Heliyon

Mathematical Modelling of Ebola with Optimal Control and Cost-Effectiveness Analysis

A fractal–fractional model of Ebola with reinfection

Results in Physics

A fractal–fractional order model for exploring the dynamics of Monkeypox disease

Decision Analytics Journal

In this paper, it is shown that discrete equations with Hilb ert matrix operator, circulant matri... more In this paper, it is shown that discrete equations with Hilb ert matrix operator, circulant matrix operator, conference matrix operator, banded matrix operator, and sparse matrix operator are ill-posed in the sense of Hadamard. These ill-posed problems cannot be regularized by Gauss Least Square Method (GLSM), QR Factorization Method (QRFM), Cholesky Decomposition Method (CDM) and Singular Value Decomposition (SVDM). To overcome the limitations of these methods of regularization, an Eigenspace Spectral Regularization Method (ESRM) is introduced which solves ill-p os ed discrete equations with Hilb ert matrix operator, circulant matrix operator, conference matrix operator, banded matrix operator, and sparse matrix operator. Unlike GLSM, QRFM, CDM, and SVDM, the ESRM regularize such a system. In addition, the ESRM has a unique property, the norm of the eigenspace spectral matrix operator κ (K) = ||K − 1K|| = 1. Thus, the condition number of ESRM is bounded by unity unlike the other re...

Non-optimal and optimal fractional control analysis of measles using real data

Informatics in medicine unlocked, Jul 1, 2024

Modelling the dynamics of Ebola disease transmission with optimal control analysis

Modeling earth systems and environment, May 23, 2024

A fractional order Ebola transmission model for dogs and humans

Scientific African, May 1, 2024

In this paper, it is shown that discrete equations with Hilb ert matrix operator, circulant matri... more In this paper, it is shown that discrete equations with Hilb ert matrix operator, circulant matrix operator, conference matrix operator, banded matrix operator, and sparse matrix operator are ill-posed in the sense of Hadamard. These ill-posed problems cannot be regularized by Gauss Least Square Method (GLSM), QR Factorization Method (QRFM), Cholesky Decomposition Method (CDM) and Singular Value Decomposition (SVDM). To overcome the limitations of these methods of regularization, an Eigenspace Spectral Regularization Method (ESRM) is introduced which solves ill-p os ed discrete equations with Hilb ert matrix operator, circulant matrix operator, conference matrix operator, banded matrix operator, and sparse matrix operator. Unlike GLSM, QRFM, CDM, and SVDM, the ESRM regularize such a system. In addition, the ESRM has a unique property, the norm of the eigenspace spectral matrix operator κ (K) = ||K − 1K|| = 1. Thus, the condition number of ESRM is bounded by unity unlike the other re...

Modelling the transmission behavior of Measles disease considering contaminated environment through a fractal-fractional Mittag-Leffler kernel

Physica scripta, May 29, 2024

Optimal control dynamics of Gonorrhea in a structured population

Heliyon

Mathematical Modelling of Ebola with Optimal Control and Cost-Effectiveness Analysis

A fractal–fractional model of Ebola with reinfection

Results in Physics

A fractal–fractional order model for exploring the dynamics of Monkeypox disease

Decision Analytics Journal

In this paper, it is shown that discrete equations with Hilb ert matrix operator, circulant matri... more In this paper, it is shown that discrete equations with Hilb ert matrix operator, circulant matrix operator, conference matrix operator, banded matrix operator, and sparse matrix operator are ill-posed in the sense of Hadamard. These ill-posed problems cannot be regularized by Gauss Least Square Method (GLSM), QR Factorization Method (QRFM), Cholesky Decomposition Method (CDM) and Singular Value Decomposition (SVDM). To overcome the limitations of these methods of regularization, an Eigenspace Spectral Regularization Method (ESRM) is introduced which solves ill-p os ed discrete equations with Hilb ert matrix operator, circulant matrix operator, conference matrix operator, banded matrix operator, and sparse matrix operator. Unlike GLSM, QRFM, CDM, and SVDM, the ESRM regularize such a system. In addition, the ESRM has a unique property, the norm of the eigenspace spectral matrix operator κ (K) = ||K − 1K|| = 1. Thus, the condition number of ESRM is bounded by unity unlike the other re...