Recently, evolutionary algorithms with global search feature are frequently used as an alternative to algorithms that require derivative knowledge in Artificial Neural Network (ANN) trainings. In this study, ANN training was carried out on Field Programmable Gate Arrays (FPGA) with the Artificial Bee Colony (ABC) algorithm, one of the evolutionary algorithms. Number format and activation function approach is important in terms of cost, speed and error sensitivity in FPGA-based implementation. In the study, IEEE 754 floating point number format, which has high sensitivity and dynamism features, was chosen. Since the hardware implementation of the exponential function is difficult, a mathematical approach was used in the hardware implementation of the activation function. In the study, ANN architecture was designed to solve the problem of vehicle license plate region detection and trained on FPGA with ABC algorithm. 98.82% success of the trained network in the test data showed that the ANN trained on FPGA made a good generalization and the synthesis results showed that the application could be realized with only 9% area consumption in FPGA.
: February 22, 2021
|APA||Çavuşlu, M . (2021). Plaka Bölgesi Tespiti Problemi için Yapay Arı Koloni Algoritması ile YSA Eğitiminin APKD’de Gerçeklenmesi . Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi , 8 (1) , 446-457 . DOI: 10.35193/bseufbd.884109|