Soft Computing

, Volume 22, Issue 11, pp 3591–3601 | Cite as

ELM-based convolutional neural networks making move prediction in Go

  • Xiangguo Zhao
  • Zhongyu Ma
  • Boyang Li
  • Zhen Zhang
  • Hengyu Liu


With the rapid development of machine learning, artificial intelligence (AI) has drawn much more attention. Under this circumstances, abstract strategy games, such as chess, checkers and Go, have been a fascinating problem of AI research. Most of the existing state-of-the-art Go programs used deep neural network technology, like convolutional neural networks (CNNs). However, CNNs require multiple iterations to optimize weights and spend a lot of training time. Therefore, in this paper, in order to solve the above shortcomings, we propose a new learning algorithm ECNN, which integrates CNNs with extreme learning machine (ELM). We remove pooling layers of CNNs and insert ELM layers between convolutional layers. The newly added ELM layers will be updated in back-propagation process, and they accelerate the convergence of weights in CNNs. Therefore, our ECNN can reduce the training time of CNNs. Further, we propose ECNN-Go algorithm, which applies ECNN to Go game. Because of the advantage of ECNN, ECNN-Go algorithm has the fast learning speed to make move prediction in Go game. Finally, the experimental results show the efficiency and accuracy of ECNN algorithm and demonstrate the strength of ECNN-Go.


Artificial intelligence Go programs Convolutional neural networks Extreme learning machine 



This research is partially supported by National Natural Science Foundation of China under Grant Nos. 61672145, 61572121, 61602323, 61702086 and U1401256, and the China Postdoctoral Science Foundation under Grant No. 2016M591455.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no potential conflict of interest.

Human participants and animals

This article does not contain any studies involving human participants and/or animals by any of the authors.

Informed consent

Informed consent was obtained from all individual participants.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Computer Science and EngineeringNortheastern UniversityShenyangChina

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