Person: VATANSEVER, FAHRİ
Loading...
Email Address
Birth Date
Research Projects
Organizational Units
Job Title
Last Name
VATANSEVER
First Name
FAHRİ
Name
3 results
Search Results
Now showing 1 - 3 of 3
Publication A new hybrid method for signal estimation based on haar transform and prony analysis(IEEE-inst Electrical Electronics Engineers Inc, 2021-01-01) Yalçın, Nedim Aktan; Vatansever, Fahri; YALÇIN, NEDİM AKTAN; VATANSEVER, FAHRİ; Bursa Uludağ Üniversitesi/Elektrik-Elektronik Mühendisliği Bölümü; 0000-0002-0049-7841; 0000-0002-3885-8622; AAH-1474-2021; AAG-8425-2021The signal estimation is very important in electrical and electronic engineering. In this study, it is shown that signal parameters' (frequency, amplitude, and phase) estimation can be realized with the implementation of Prony method on Haar transform coefficients. In order to accomplish this, mathematical relationship between roots of Prony polynomial which are found with original signal values and roots which are calculated with Haar approximation/detail coefficients is constructed. Frequency components of signal are estimated with this relationship. Next, the second part of Prony algorithm which constructs the matrix equation between roots and signal values in order to find the amplitude and phase values is implemented with Haar coefficients. In other words, a new matrix equation is derived for finding amplitudes and phases with the found roots in the first step and Haar coefficients. Thus, implementations of the first and second steps give signal parameters. Derived equations are valid for all degrees of Haar coefficients not just the first one. The use of Haar coefficients decreases the data size and increases the speed and accuracy. The proposed method is also more robust of selection of different Prony polynomial coefficient sizes.Publication Modified forensic-based investigation algorithm for global optimization(Springer, 2021-02-26) Kuyu, Yiğit Çağatay; Vatansever, Fahri; KUYU, YİĞİT ÇAĞATAY; VATANSEVER, FAHRİ; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Elektrik-Elektronik Mühendisliği Bölümü; 0000-0002-3885-8622; 0000-0002-7054-3102; AAG-8425-2021; AAC-6923-2021Forensic-based investigation (FBI) is recently developed metaheuristic algorithm inspired by the suspect investigation-location-pursuit operations of police officers. This study focuses on the search processes of the FBI algorithm, called Step A and Step B, to improve and increase its performance. For this purpose, opposition-based learning is adopted to Step A to enhance diversity, while Cauchy-based mutation is integrated with Step B to guide the search to different regions and to jump out of local minima. To show the effectiveness of these improvements, the proposed algorithm has been tested with two different benchmark sets. To verify the performance of the new modified algorithm, the statistical test is carried out on numerical functions. This study also investigates the application of the proposed algorithm to a set of six real-world problems. The proposed and adapted/integrated methods appear to have a significant impact on the FBI algorithm, which augments its performance, resulting in better solutions than the compared algorithms in most of the functions and real-world problems.Publication Advanced metaheuristic algorithms on solving multimodal functions: Experimental analyses and performance evaluations(Springer, 2021-12) Kuyu, Yiğit Çağatay; Vatansever, Fahri; KUYU, YİĞİT ÇAĞATAY; VATANSEVER, FAHRİ; Bursa Uludağ Üniversitesi/Elektrik Mühendisliği Bölümü; 0000-0002-3885-8622; 0000-0002-7054-3102; AAC-6923-2021; AAG-8425-2021Optimization problems encountered in real-world have multiple local minimums. Multimodal functions can well represent many real-world applications as they include two or more local minimum points in nature. Numerous metaheuristic algorithms aim to find the best balance between exploration and exploitation, and better algorithms have been developed during the search for such a balance. Therefore, it becomes necessary to answer the question: Which metaheuristic algorithm is the best-suited algorithm among the metaheuristics that have been developed? This study presents a comprehensive and fair investigation of the seven metaheuristic algorithms developed in the last five years on twenty multimodal functions with a wide range of dimensions commonly used in literature. Each is subject to the same initial conditions but with three different performance criteria. The strengths and weaknesses of the each algorithm were demonstrated for each criterion and the experimental results were analyzed statistically by using the Friedman test. Furthermore, to the best of our knowledge, this is the first attempt to address these challenging problems, in combination with these algorithms and performance metrics, which can also give a further insight to the researchers for choosing appropriate algorithms in the context of global optimization.