DESIGN OF INTELLIGENT CRUISE CONTROLLER OF MOTOR VEHICLES

Nowadays, due to traffic jam and many cars on traffic, it is very necessary to control the distance between cars and obstacle. Many car producers have been designed and manufacture Cruise Control Systems for cars. Reinforcement learning, one of the popular artificial intelligence techniques, is a method used to train autonomous systems in many different fields. In this simulation study, the adaptive cruise control (ACC) of a ring bus serving in the campus area is controlled with Deep Deterministic Policy Gradient, which is one of the reinforcement learning methods. This simulation study is carried out considering the speed limit in the campus area and the acceleration values required for a comfortable journey of the passengers. Acceleration, velocity and distance values are given with graphs. Consequently; the proposal neural predictor has superior performance to adapt and predict the distance, velocity and acceleration of ego vehicle (bus)

Mobil Robot Kontrolör Tasarımı ve Kazanç Değerlerinin Yapay Sinir Ağı Tahmincisi ile Belirlenmesi

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NEURAL PREDICTOR DESIGN FOR COVID-19 CASES IN DIFFERENT REGIONS

COVID-19, which emerged in the past years, has affected human life in many different ways. The COVID-19 virus has spread very quickly around the world and has become a pandemic. In many applications, artificial neural networks are used to estimate system parameters in real-time or simulation-based methods. In this study, the daily and total number of cases in Turkey, Italy and India are predicted. Three alternative areas, with or without following rules, are chosen for the COVID-19 cases. For this prediction process, 3 different neural network methods are used: Nonlinear autoregressive neural network (NAR-NN), Adaptive-Network Based Fuzzy Inference Systems (ANFIS) and Autoregressive integrated moving average (ARIMA). The results obtained for 3 different neural networks are given with graphs and tables. The conclusion of this study may be used to improve the precaution for the pandemic