Using Active Learning in Motor Control and Matlab Simulation
Ercan Nurcan Yilmaz 1 and
Onur Battal 2
1. Department of Electrical & Electronic Engineering, Faculty of Technology, Gazi University, Teknikokullar, Ankara, Turkey
2. Haci Bektas Veli Vocational School, Nevşehir University, Hacibektas, Nevsehir, Turkey
2. Haci Bektas Veli Vocational School, Nevşehir University, Hacibektas, Nevsehir, Turkey
Abstract—People's desire to produce systems capable of learning and decision making, has led to the concept of artificial intelligence. One of the many ways that is used in the design of intelligent systems is artificial neural networks. Artificial neural networks are computational networks which attempt to simulate the networks of nerve cell (neurons) like central nervous system of the living. Designing any intelligent control system is possible by modeling. In this paper, solid modeling, and the four axes of motion of a robot arm, the starting point positioning with servo motors of the simulation of neural networks with active learning strategies which types of learning are presented.
Index Terms—active learning, artificial neural networks, servo motor
Cite: Ercan Nurcan Yilmaz and Onur Battal, "Using Active Learning in Motor Control and Matlab Simulation," Jounal of Automation and Control Engineering, Vol. 3, No. 4, pp. 299-303, August, 2015. doi: 10.12720/joace.3.4.299-303
Index Terms—active learning, artificial neural networks, servo motor
Cite: Ercan Nurcan Yilmaz and Onur Battal, "Using Active Learning in Motor Control and Matlab Simulation," Jounal of Automation and Control Engineering, Vol. 3, No. 4, pp. 299-303, August, 2015. doi: 10.12720/joace.3.4.299-303