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Volume 6 Issue 6
Nov.  2019

IEEE/CAA Journal of Automatica Sinica

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Chengdong Li, Jianqiang Yi, Yisheng Lv and Peiyong Duan, "A Hybrid Learning Method for the Data-Driven Design of Linguistic Dynamic Systems," IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1487-1498, Nov. 2019. doi: 10.1109/JAS.2019.1911543
Citation: Chengdong Li, Jianqiang Yi, Yisheng Lv and Peiyong Duan, "A Hybrid Learning Method for the Data-Driven Design of Linguistic Dynamic Systems," IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1487-1498, Nov. 2019. doi: 10.1109/JAS.2019.1911543

A Hybrid Learning Method for the Data-Driven Design of Linguistic Dynamic Systems

doi: 10.1109/JAS.2019.1911543

the National Natural Science Foundation of China 61473176

the National Natural Science Foundation of China 61773246

Natural Science Foundation of Shandong Province for Outstanding Young Talents in Provincial Universities ZR2015JL021

the Taishan Scholar Project of Shandong Province TSQN201812092

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  • In lots of data based prediction or modeling applications, uncertainties and/or noises in the observed data cannot be avoided. In such cases, it is more preferable and reasonable to provide linguistic (fuzzy) predicted results described by fuzzy memberships or fuzzy sets instead of the crisp estimates depicted by numbers. Linguistic dynamic system (LDS) provides a powerful tool for yielding linguistic (fuzzy) results. However, it is still difficult to construct LDS models from observed data. To solve this issue, this paper first presents a simplified LDS whose inputoutput mapping can be determined by closed-form formulas. Then, a hybrid learning method is proposed to construct the data-driven LDS model. The proposed hybrid learning method firstly generates fuzzy rules by the subtractive clustering method, then carries out further optimization of centers of the consequent triangular fuzzy sets in the fuzzy rules, and finally adopts multiobjective optimization algorithm to determine the left and right end-points of the consequent triangular fuzzy sets. The proposed approach is successfully applied to three real-world prediction applications which are: prediction of energy consumption of a building, forecasting of the traffic flow, and prediction of the wind speed. Simulation results show that the uncertainties in the data can be effectively captured by the linguistic (fuzzy) estimates. It can also be extended to some other prediction or modeling problems, in which observed data have high levels of uncertainties.


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    • A simplified linguistic dynamic system (LDS) which has closed-form expression is presented.
    • A hybrid learning method is proposed to construct the data-driven LDS model.
    • The LDS is applied to the linguistic predictions of the energy consumption, traffic flow and wind speed.
    • The proposed approach is easy to implement because it requires minimal human intervention.


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