Training Artificial Neural Networks and Convolutional Neural Networks using WFSO Algorithm

WFSO 알고리즘을 이용한 인공 신경망과 합성곱 신경망의 학습

  • Jang, Hyun-Woo (Department of Electronics and Information Engineering, Hansung University) ;
  • Jung, Sung Hoon (School of Mechanical and Electronic Engineering, Hansung University)
  • 장현우 (한성대학교 전자정보공학과) ;
  • 정성훈 (한성대학교 기계전자공학부)
  • Received : 2017.08.19
  • Accepted : 2017.08.31
  • Published : 2017.08.31


This paper proposes the learning method of an artificial neural network and a convolutional neural network using the WFSO algorithm developed as an optimization algorithm. Since the optimization algorithm searches based on a number of candidate solutions, it has a drawback in that it is generally slow, but it rarely falls into the local optimal solution and it is easy to parallelize. In addition, the artificial neural networks with non-differentiable activation functions can be trained and the structure and weights can be optimized at the same time. In this paper, we describe how to apply WFSO algorithm to artificial neural network learning and compare its performances with error back-propagation algorithm in multilayer artificial neural networks and convolutional neural networks.


Supported by : 한성대학교


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