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Wyszukujesz frazę "Liu, Chunzhi" wg kryterium: Autor


Wyświetlanie 1-2 z 2
Tytuł:
Fuzzy comprehensive model of manufacturing industry transfer risk based on economic big data analysis
Autorzy:
Sun, Tong
Liu, Chunzhi
Powiązania:
https://bibliotekanauki.pl/articles/2173644.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
economic big data
manufacturing industry
industrial transfer risk
entropy weight method
fuzzy model
duże zbiory danych ekonomicznych
przemysł wytwórczy
ryzyko transferu przemysłowego
metoda wag entropii
model rozmyty
Opis:
Aiming at the problems of low accuracy, low efficiency and low stability of traditional methods and recent developments in advanced technology incite the industries to be in sync with modern technology. With respect to various available techniques, this paper designs a fuzzy comprehensive evaluation model of the manufacturing industry for transferring risk based on economic big-data analytics. The big-data analysis method is utilized to obtain the data source of fuzzy evaluation of the manufacturing industry to transfer risk using data as the basis of risk evaluation. Based on the risk factors, the proposed model establishes the risk index system of the manufacturing industry and uses the expert evaluation method to design the scoring method of the evaluation index system. To ensure the accuracy of the evaluation results, the manufacturing industry’s fuzzy comprehensive model is established using the entropy weight method, and the expert evaluation results are modified accordingly. The experimental results show that the highest efficiency of the proposed method is 96%, the highest accuracy of the evaluation result is 75%. The evaluation result’s stability is higher than the other existing methods, which fully verifies the effectiveness and can provide a reliable theoretical basis for enterprise risk evaluation research.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 2; art. no. e139959
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Forecasting short-term electric load using extreme learning machine with improved tree seed algorithm based on Lévy flight
Autorzy:
Chen, Xuan
Przystupa, Krzysztof
Ye, Zhiwei
Chen, Feng
Wang, Chunzhi
Liu, Jinhang
Gao, Rong
Wei, Ming
Kochan, Orest
Powiązania:
https://bibliotekanauki.pl/articles/2087016.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
short-term electric load forecast
extreme learning machine
Lévy flight
tree-seed algorithm
Kernel principal component analysis
Opis:
In recent years, forecasting has received increasing attention since it provides an important basis for the effective operation of power systems. In this paper, a hybrid method, composed of kernel principal component analysis (KPCA), tree seed algorithm based on Lévy flight (LTSA) and extreme learning machine (ELM), is proposed for short-term load forecasting. Specifically, the randomly generated weights and biases of ELM have a significant impact on the stability of prediction results. Therefore, in order to solve this problem, LTSA is utilized to obtain the optimal parameters before the prediction process is executed by ELM, which is called LTSA-ELM. Meanwhile, the input data is extracted by KPCA considering the sparseness of the electric load data and used as the input of LTSA-ELM model. The proposed method is tested on the data from European network on intelligent technologies (EUNITE) and experimental results demonstrate the superiority of the proposed approaches compared to the other methods involved in the paper.
Źródło:
Eksploatacja i Niezawodność; 2022, 24, 2; 153--162
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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