著者

Takahiro Komai Song-Ju Kim Takuji Kousaka Hiroaki Kurokawa

出版者

Research Institute of Signal Processing, Japan

雑誌

Journal of Signal Processing (ISSN:13426230)

巻号頁・発行日

vol.23, no.4, pp.177-180, 2019-07-20 (Released:2019-07-20)

参考文献数

5

被引用文献数

2

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In our previous studies, we showed that the estimation of the rock-scissors-paper (RSP, janken) game strategy is effective for the prediction of a player's hand sign sequences. The purpose of this study is to propose a method to estimate the RSP game strategy in the basis of human personality in an RSP game. To estimate a player's strategy in the RSP game, it is effective to compare the player's hand sign sequence and the hand sign sequences given by various typical RSP strategies on the basis of similarity. In this study, we propose the method of using a homology search to calculate the similarity between sequences. The results show that our proposed method is effective for strategy estimation.

著者

Naoyuki Kizaki Hiroshi Yoshino Hiroaki Kurokawa

出版者

The Institute of Electronics, Information and Communication Engineers

雑誌

Nonlinear Theory and Its Applications, IEICE (ISSN:21854106)

巻号頁・発行日

vol.6, no.2, pp.226-236, 2015 (Released:2015-04-01)

参考文献数

19

被引用文献数

1 2

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The regulatory interactions among genes are summarized by the gene regulatory network. Recently, the gene regulatory network that is described by the differential equations is widely used, and a lot of inference methods using time course data of the gene expression levels have been proposed. One of the successful inference methods of the gene regulatory network is the method using the neural network. In this study, as a method to improve a performance of the gene regulatory network inference using the neural networks, we propose the method to apply a kind of majority rule to the conventional method. Our proposed method infers the regulatory interactions in the gene network based on the results of a lot of trials of the inference using neural networks. In the simulations, we evaluate our proposed method using artificially defined gene regulatory networks. The results show the validity of the proposed method. The results also suggest that the strategy of the proposed method is applicable to various methods using the heuristic solver.