Autor: Zhang, Cheng - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Neuro-adaptive cooperative control for high-order nonlinear multi-agent systems with uncertainties
Autorzy:: Peng, Cheng
Zhang, Anguo
Li, Junyu
Powiązania:: https://bibliotekanauki.pl/articles/2055174.pdf
Data publikacji:: 2021
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: multiagent system
radial basis function
RBF neural network
sliding mode control
cooperative control
system wieloagentowy
radialna funkcja bazowa
sieć neuronowa RBF
sterowanie ślizgowe
Opis:: The consensus problem for a class of high-order nonlinear multi-agent systems (MASs) with external disturbance and system uncertainty is studied. We design an online-update radial basis function (RBF) neural network based distributed adaptive control protocol, where the sliding model control method is also applied to eliminate the influence of the external disturbance and system uncertainty. System consensus is verified by using the Lyapunov stability theorem, and sufficient conditions for cooperative uniform ultimately boundedness (CUUB) are also derived. Two simulation examples demonstrate the effectiveness of the proposed method for both homogeneous and heterogeneous MASs.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2021, 31, 4; 635--645
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Bounded-abstaining classification for breast tumors in imbalanced ultrasound images
Autorzy:: Guan, Hongjiao
Zhang, Yingtao
Cheng, Heng-Da
Tang, Xianglong
Powiązania:: https://bibliotekanauki.pl/articles/330479.pdf
Data publikacji:: 2020
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: breast ultrasound image
reliable diagnosis
abstaining classification
imbalanced dataset
ultrasonografia piersi
diagnoza wiarygodna
zbiór danych niezrównoważony
Opis:: Computer-aided breast ultrasound (BUS) diagnosis remains a difficult task. One of the challenges is that imbalanced BUS datasets lead to poor performance, especially with regard to low accuracy in the minority (malignant tumor) class. Missed diagnosis of malignant tumors can cause serious consequences, such as delaying treatment and increasing the risk of death. Moreover, many diagnosis methods do not consider classification reliability; thus, some classifications may have a large uncertainty. To resolve such problems, a bounded-abstaining classification model is proposed. It maximizes the area under the ROC curve (AUC) under two abstention constraints. A total of 219 (92 malignant and 127 benign) BUS images are collected from the First Affiliated Hospital of Harbin Medical University, China. The experiment tests BUS datasets of three imbalance levels, and the performance contours are analyzed. The results demonstrate that AUC-rejection curves are less affected by class imbalance than accuracy-rejection curves. Compared with the state-of-the-art, the proposed method yields a significantly larger AUC and G-mean using imbalanced BUS datasets.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2020, 30, 2; 325-336
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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