6 Papers
Yunfa Fu is an academic researcher from Kunming University of Science and Technology. The author has contributed to research in topics: Population & Stability (learning theory). The author has an hindex of 1, co-authored 6 publications.
Chat about Author
Papers
Parameters identification of photovoltaic cells using improved version of the chaotic grey wolf optimizer
TL;DR: A new enhanced optimization method is proposed to estimate the unknown parameters of photovoltaic modules by combining the adaptive grey wolf optimization (AGWO) and chaotic greywolf optimization (CGWO) algorithms and the results confirm accuracy, robustness, and high convergence speed in comparison with some well-known optimization methods.
30
Interactive modulations between congruency sequence effects and validity sequence effects.
TL;DR: In this article, a modified attentional network test (ANT) was used to investigate the relationship between congruency sequence effects and validity sequence effects (VSE) in both conflict and spatial orienting tasks, and it was found that the sequence effects in these networks are possibly controlled by a complex and multifaceted adaptive control mechanism.
1
BPSO Algorithm with Opposition-Based Learning Method for Association Rule Mining
Qianyi Zhong,Qian Qian,Yong Feng,Yunfa Fu +3 more
- 01 Jan 2021
TL;DR: In this paper, a binary particle swarm optimization algorithm is proposed to improve the association rule mining problem, which does not need to manually specify support and confidence thresholds and uses an opposition-based learning method to reduce the probability of the algorithm falling into local extreme.
1
Adaptive Parallel Flower Pollination Algorithm
Xin Geng,Qian Qian,Yong Feng,Yunfa Fu +3 more
- 01 Jan 2021
TL;DR: Wang et al. as discussed by the authors proposed an improved adaptive parallel flower pollination algorithm to improve the shortcomings of insufficient diversity of a single population in the middle and late stages of the calculation, which can further strengthen the algorithm's global search ability while keeping the same local search ability.
Multi-Population Genetic Algorithm Based on Adaptive Learning Mechanism
Jiawen Pan,Qian Qian,Yong Feng,Yunfa Fu +3 more
- 01 Jan 2021
TL;DR: Wang et al. as mentioned in this paper improved the learning mechanism by adaptively changing the related control parameters, and dynamically controlling the process of learning mechanism, which has great improvement in many aspects of the global optimization, such as convergence speed, the accuracy of the solution, and stability.