Application of Adaptive Artificial Neural Network Method to Model the Excitation Currents of Synchronous Motors


11th IEEE International Conference on Machine Learning and Applications (ICMLA), Florida, United States Of America, 12 - 15 December 2012, pp.498-502 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/icmla.2012.167
  • City: Florida
  • Country: United States Of America
  • Page Numbers: pp.498-502
  • Keywords: Synchronous Motor, Adaptive Artificial Neural Network, Excitation Current Estimation, Genetic Algorithm, POWER-FACTOR CORRECTION, K-NN ESTIMATOR
  • Karadeniz Technical University Affiliated: Yes


In the classic ANN-based approaches, the synchronous motor parameters mostly could be modeled with n-hidden layered networks. It is an important challenge in driver software development is to realize complex mathematical models in real time environments and circuits. This paper presents an Adaptive Artificial Neural Network-based (AANN) method to easily model excitation current of synchronous motors. It has a simple network structure and less processing units (nodes) more than classic ANN. The main purpose of this method are to estimate the excitation current and also to assist designers to model excitation current easily and to develop complex driver software with low degree programming effort while improving the efficiency of classic ANN-based approach. In the adopted approach, the activation functions of nodes in the hidden layers of multilayered feed forward neural network have been determined by using a heuristic method. The experimental results have shown that the proposed method successfully creates single-hidden layered simple networks have less node number than classic ANN-based solutions and achieves the tasks in high estimation accuracies.