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A conditional classification recurrent RBM for improved series mid-term forecasting

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Abstract

In the context of human-robot and robot-robot interactions, the better cooperation can be achieved by predicting the other party’s subsequent actions based on the current action of the other party. The time duration for adjustment is not sufficient provided by short term forecasting models to robots. A longer duration can by achieved by mid-term forecasting. But the mid-term forecasting models introduce the previous errors into the follow-up forecasting and amplified gradually, eventually invalidating the forecasting. A new mid-term forecasting with error suppression based on restricted Boltzmann machine(RBM) is proposed in this paper. The proposed model can suppress the error amplification by replacing the previous inputs with their features, which are retrieved by a deep belief network(DBN). Furthermore, a new mechanism is proposed to decide whether the forecasting result is accepted or not. The model is evaluated with several datasets. The reported experiments demonstrate the superior performance of the proposed model compared to the state-of-the-art approaches.

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Funding

This work is supported by the Key Program of National Science Foundation of China (Grant No. 61836006) and partially supported by National Natural Science Fund for Distinguished Young Scholar (Grant No. 61625204).

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Authors and Affiliations

  1. Computer Science College, Sichuan University, Sichuan, China

    Lei Xia, Jiancheng Lv, Chunzhi Xie & Jing Yin

  2. The Machine Intelligence Laboratory, No. 24 South Section 1, Yihuan Road, Chengdu, Sichuan, 610005, China

    Lei Xia, Jiancheng Lv, Chunzhi Xie & Jing Yin

  3. Information and Software Engineering Department, Chengdu Neusoft University, Chengdu, China

    Lei Xia

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  1. Lei Xia
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  2. Jiancheng Lv
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  3. Chunzhi Xie
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  4. Jing Yin
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Correspondence to Jiancheng Lv.

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Xia, L., Lv, J., Xie, C. et al. A conditional classification recurrent RBM for improved series mid-term forecasting. Appl Intell 51, 8334–8348 (2021). https://doi.org/10.1007/s10489-021-02315-4

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