http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Qianwen Xie,Xueyin Chen,Jingmin Xiao,Shaonan Liu,Lihong Yang,Jing Chen,Jiaqi Lai,Rui Lan,Yi Chen,Haifang Yang,Xinfeng Guo 한국한의학연구원 2020 Integrative Medicine Research Vol.9 No.4
Background: The evidence of Acupuncture combined with speech rehabilitation training for post-stroke dysarthria is insufficient and there is no consensus on its efficacy. Methods: We searched seven Chinese and English medicine databases for randomized controlled trials (RCTs) from their inception to November 2019. The primary outcome measure was the clinical response rate, assessed with the Frenchay Dysarthria Assessment (FDA) tool. We assessed risk of bias using the Cochrane risk-of-bias tool. We used GRADE to assess the certainty of evidence (CoE). Results: Thirty studies were included in this systematic review, 23 of which were pooled in meta-analysis. Acupuncture combined with speech rehabilitation training is likely beneficial for was response rate (n = 1685; RR = 1.37; 95% CI [1.29, 1.46], P < 0.01, I2 = 34%; 17 studies, low CoE) compared to speech rehabilitation treatment alone. Conclusion: The combination of acupuncture and speech rehabilitation training may improve total response rate of stroke patients with dysarthria. However, more RCTs with rigorous study design and validated outcome measures are needed to confirm the evidence.
Meta-Cognitive Fuzzy Neural Network Control for Active Power Filter
Ming Yang,Jienan Han,Qianwen Xie 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
In this paper, an adaptive robust meta-cognitive fuzzy-neural-network control using sliding mode control is proposed for active power filter (APF), whereby a meta-cognitive fuzzy-neural-network (MCFNN) is employed to approximate modeling uncertainties. To overcome the drawbacks of conventional sliding mode control, the control law is constructed based on MCFNN rather than the actual systems. Unlike the predefined structure approaches, MCFNN is able to adapt the structure and parameters of the networks using the input information. Moreover, only the parameters of the rule nearest to the current input are adjusted online to reduce redundant or inefficient computation. Subsequently the Lyapunov stability analysis is presented to guarantee tracking performance and stability of the closed-loop system. Simulation studies demonstrate that the proposed control methods exhibit excellent performance in both steady state and transient operation.
Modified Recurrent Fuzzy Neural Network Sliding Mode Control for Nonlinear Systems
Yundi Chu,Ming Yang,Jienan Han,Qianwen Xie 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
In this study, a modified fuzzy neural network sliding mode control for a class of nonlinear systems is designed. Firstly, the considered nonlinear system is given and a global sliding mode control is proposed. Then, a modified fuzzy neural network (FNN) is constructed and utilized for estimating the uncertain function. Compared with the conventional FNN, the designed FNN can obtain a better generalization capability with two feedback loops. Moreover, the stability analysis in the Lyapunov framework is implemented to ensure the zero-error-convergence performance. To validate the control performance of the proposed scheme, active power filter is selected as the controlled plant. The simulation results demonstrate that the designed control method can achieve superior control capability.