Zobrazeno 1 - 10
of 87
pro vyhledávání: '"Auwal Bala Abubakar"'
Publikováno v:
Results in Control and Optimization, Vol 17, Iss , Pp 100483- (2024)
This paper proposes a hybridized Brazilian and Bowein derivative-free spectral gradient projection method for solving systems of convex-constrained nonlinear equations. The method avoids solving any subproblems in each iteration. Global convergence i
Externí odkaz:
https://doaj.org/article/a709a60c7d384a7ca9ac8fe0d95dfafa
Publikováno v:
AIMS Mathematics, Vol 8, Iss 1, Pp 1-28 (2023)
The conjugate gradient (CG) method is an optimization method, which, in its application, has a fast convergence. Until now, many CG methods have been developed to improve computational performance and have been applied to real-world problems. In this
Externí odkaz:
https://doaj.org/article/fdeb6935e4884e2b90b392929720031e
Biometric Information Recognition Using Artificial Intelligence Algorithms: A Performance Comparison
Autor:
Sunusi Bala Abdullahi, Chainarong Khunpanuk, Zakariyya Abdullahi Bature, Haruna Chiroma, Nuttapol Pakkaranang, Auwal Bala Abubakar, Abdulkarim Hassan Ibrahim
Publikováno v:
IEEE Access, Vol 10, Pp 49167-49183 (2022)
Addressing crime detection, cyber security and multi-modal gaze estimation in biometric information recognition is challenging. Thus, trained artificial intelligence (AI) algorithms such as Support vector machine (SVM) and adaptive neuro-fuzzy infere
Externí odkaz:
https://doaj.org/article/26557755856c4446b625373894d81216
Publikováno v:
Journal of Inequalities and Applications, Vol 2021, Iss 1, Pp 1-25 (2021)
Abstract In recent times, various algorithms have been incorporated with the inertial extrapolation step to speed up the convergence of the sequence generated by these algorithms. As far as we know, very few results exist regarding algorithms of the
Externí odkaz:
https://doaj.org/article/2bd3fc6e4f1d440cbee76074c9661992
Autor:
Abdulkarim Hassan Ibrahim, Poom Kumam, Auwal Bala Abubakar, Umar Batsari Yusuf, Seifu Endris Yimer, Kazeem Olalekan Aremu
Publikováno v:
AIMS Mathematics, Vol 6, Iss 1, Pp 235-260 (2021)
Motivated by the projection technique, in this paper, we introduce a new method for approximating the solution of nonlinear equations with convex constraints. Under the assumption that the associated mapping is Lipchitz continuous and satisfies a wea
Externí odkaz:
https://doaj.org/article/974220a6156644a48e1eee12bee1b28a
Autor:
Auwal Bala Abubakar, Maulana Malik, Poom Kumam, Hassan Mohammad, Min Sun, Abdulkarim Hassan Ibrahim, Aliyu Ibrahim Kiri
Publikováno v:
Journal of King Saud University: Science, Vol 34, Iss 4, Pp 101923- (2022)
Conjugate gradient methods have played a vital role in finding the minimizers of large-scale unconstrained optimization problems due to the simplicity of their iteration, convergence properties and their low memory requirements. Based on the Liu-Stor
Externí odkaz:
https://doaj.org/article/07b419ad0f9b44dd99d4942c99ed52d8
Publikováno v:
AIMS Mathematics, Vol 6, Iss 6, Pp 6506-6527 (2021)
In this paper, we present a new hybrid conjugate gradient (CG) approach for solving unconstrained optimization problem. The search direction is a hybrid form of the Fletcher-Reeves (FR) and the Dai-Yuan (DY) CG parameters and is close to the directio
Externí odkaz:
https://doaj.org/article/5d633a851a4e43039c9b06f9da236ba0
Autor:
Sunusi Bala Abdullahi, Kanikar Muangchoo, Auwal Bala Abubakar, Abdulkarim Hassan Ibrahim, Kazeem Olalekan Aremu
Publikováno v:
IEEE Access, Vol 9, Pp 55388-55412 (2021)
Adaptive Neuro-fuzzy Inference System (ANFIS) remains one of the promising AI techniques to handle data over-fitting and as well, improves generalization. Presently, many ANFIS optimization techniques have been synergized and found effective at some
Externí odkaz:
https://doaj.org/article/72369091b7354f039728e672b90d6395
Autor:
Auwal Bala Abubakar, Kanikar Muangchoo, Abdulkarim Hassan Ibrahim, Abubakar Bakoji Muhammad, Lateef Olakunle Jolaoso, Kazeem Olalekan Aremu
Publikováno v:
IEEE Access, Vol 9, Pp 18262-18277 (2021)
In this article, a derivative-free method of Hestenes-Stiefel type is proposed for solving system of monotone operator equations with convex constraints. The method proposed is matrix-free, and its sequence of search directions are bounded and satisf
Externí odkaz:
https://doaj.org/article/cc584b7b566e4fc385ee1f27644585fb
Publikováno v:
IEEE Access, Vol 9, Pp 92157-92167 (2021)
In this paper, we propose an inertial derivative-free projection method for solving convex constrained nonlinear monotone operator equations (CNME). The method incorporates the inertial step with an existing method called derivative-free projection (
Externí odkaz:
https://doaj.org/article/028050ce56a345b88158dcf3157babf3