http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Zhongda Wang (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
-
Multi-objective optimization of drive gears for power split device using surrogate models
Jixin Wang,Wanghao Shen,Zhongda Wang,Mingyao Yao,Xiaohua Zeng 대한기계학회 2014 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.28 No.6
Power split device (PSD) is a key component in the energy coupling and decoupling of parallel-series hybrid electric vehicle. This paperproposes a multi-objective optimization method to achieve optimal balance solution among the volume, contact stress, and frictionalenergy dissipation of PSD drive gears, some of which are implicit with respect to design variables. To avoid the time-consuming problemof finite element analysis used to solve nonlinear responses, surrogate models are adopted to generate approximate expressions of designvariables. Pareto-optimal solutions of PSD are obtained using multi-island genetic algorithm (GA), non-dominated sorting GA-II(NSGA-II), and multi-objective particle swarm optimization algorithm. The performances of PSD before and after optimization are compared. Results indicate that the proposed method is effective, and NSGA-II achieves higher optimizing efficiency in solving the multiobjectiveoptimization problem of PSD than the other algorithms.
-
Zhongda Tian,Yi Ren,Gang Wang 대한전기학회 2018 Journal of Electrical Engineering & Technology Vol.13 No.5
For the safe and stable operation of the power system, accurate wind power prediction is of great significance. A wind power prediction method based on empirical mode decomposition and improved extreme learning machine is proposed in this paper. Firstly, wind power time series is decomposed into several components with different frequency by empirical mode decomposition, which can reduce the non-stationary of time series. The components after decomposing remove the long correlation and promote the different local characteristics of original wind power time series. Secondly, an improved extreme learning machine prediction model is introduced to overcome the sample data updating disadvantages of standard extreme learning machine. Different improved extreme learning machine prediction model of each component is established. Finally, the prediction value of each component is superimposed to obtain the final result. Compared with other prediction models, the simulation results demonstrate that the proposed prediction method has better prediction accuracy for wind power.
-
Tian, Zhongda,Ren, Yi,Wang, Gang The Korean Institute of Electrical Engineers 2018 Journal of Electrical Engineering & Technology Vol.13 No.5
For the safe and stable operation of the power system, accurate wind power prediction is of great significance. A wind power prediction method based on empirical mode decomposition and improved extreme learning machine is proposed in this paper. Firstly, wind power time series is decomposed into several components with different frequency by empirical mode decomposition, which can reduce the non-stationary of time series. The components after decomposing remove the long correlation and promote the different local characteristics of original wind power time series. Secondly, an improved extreme learning machine prediction model is introduced to overcome the sample data updating disadvantages of standard extreme learning machine. Different improved extreme learning machine prediction model of each component is established. Finally, the prediction value of each component is superimposed to obtain the final result. Compared with other prediction models, the simulation results demonstrate that the proposed prediction method has better prediction accuracy for wind power.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
