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21.
本文在提出一种水电站水库优化调度准则的基础上,建立了考虑环境因素影响的水电站水库模糊优化调度模型,实例研究表明,所建模型是有效的、适用的。 相似文献
22.
数控机床交流伺服系统的复合控制研究 总被引:1,自引:0,他引:1
针对常规P-P与PPI控制存在的问题,提出了在数控机床交流伺服系统中,采用带速度和加速度作为前馈控制,模糊自校正PID控制作为位置反馈控制的复合控制器,实验证明可以显著提高控制系统的控制精度,大大降低跟踪误差,具有较强的鲁棒性。 相似文献
23.
24.
Speed-up fractal image compression with a fuzzy classifier 总被引:4,自引:0,他引:4
This paper presents a fractal image compression scheme incorporated with a fuzzy classifier that is optimized by a genetic algorithm. The fractal image compression scheme requires to find matching range blocks to domain blocks from all the possible division of an image into subblocks. With suitable classification of the subblocks by a fuzzy classifier we can reduce the search time for this matching process so as to speedup the encoding process in the scheme. Implementation results show that by introducing three image classes and using fuzzy classifier optimized by a genetic algorithm the encoding process can be speedup by about 40% of an unclassified encoding system. 相似文献
25.
文章提出了一种在强背景噪声环境下对被淹没的谐波信号进行模糊滤波,从而恢复微弱目标信号的方法。在MATLAB环境下,先利用相干平均法,除去被测信号中的随机白噪声,对于有色噪声运用自适应神经模糊推理系统(ANFIS)的方法,对被测信号进行模糊滤波,在较低信噪比下,能较好的恢复出原始微弱信号。 相似文献
26.
南楠 《电脑与微电子技术》2012,(2):54-56,65
城市交通拥挤是当今世界普遍关注的问题,它所带来的严重危害日益影响到人们的日常生活和社会经济的发展,而城市交通信号控制系统对解决这一问题发挥着不可替代的作用。从高性能智能交通信号控制系统的要求出发,针对目前我国城市交通问题的现状.对智能交通信号协调控制系统进行初步的研究和探讨。 相似文献
27.
翟剑锋 《电脑编程技巧与维护》2012,(14):40-42
将自组织映射神经网络(SOM)与FCM结合,利用SOM的并行计算能够减少模糊C均值算法在处理海量数据时的聚类时间,可以提高聚类算法的速度和效果,同时使用该算法对校园网Web日志进行数据挖掘,能够对用户行为进行分析,从而提出相应的方法,更好地提高服务效率和管理质量。 相似文献
28.
Because the oceanaut plays a significant role in safety and capability during manned deep-diving scientific tasks, preventing oceanaut performance decline is of paramount importance. However, the factors responsible for oceanaut performance are almost entirely unexplored. To address the preceding issues, a quantitative method of fuzzy integrated Bayesian network (FIBN) was modeled within the limits of oceanaut operating procedures. To quantify the probabilities of the influencing factors, the probability of each node in the FIBN was calculated using integrated expert judgement, fuzzy logic theory, and Bayesian network. By considering a total of 28 factors related to oceanaut performance in the “Jiaolong” manned submersible, this study found that difficult sampling, long sampling times, cabin equipment failure, oceanaut physical decline, and declining decision-making ability are important factors that affect oceanaut performance. The FIBN proposed in our study fused the qualitative and quantitative methods and can be developed into a versatile tool for analysis of comprehensive systems that contain both static and dynamic factors.Relevance to industryThe results provide a powerful basis for the design of manned submersible and assignment of tasks to oceanauts, while the fuzzy integrated Bayesian network (FIBN) method proposed can be effectively applied to various quantitative assessment fields which direct researchers to deal with analysis problems of complex systems. 相似文献
29.
Scheduling semiconductor wafer manufacturing systems has been viewed as one of the most challenging optimization problems owing to the complicated constraints, and dynamic system environment. This paper proposes a fuzzy hierarchical reinforcement learning (FHRL) approach to schedule a SWFS, which controls the cycle time (CT) of each wafer lot to improve on-time delivery by adjusting the priority of each wafer lot. To cope with the layer correlation and wafer correlation of CT due to the re-entrant process constraint, a hierarchical model is presented with a recurrent reinforcement learning (RL) unit in each layer to control the corresponding sub-CT of each integrated circuit layer. In each RL unit, a fuzzy reward calculator is designed to reduce the impact of uncertainty of expected finishing time caused by the rematching of a lot to a delivery batch. The results demonstrate that the mean deviation (MD) between the actual and expected completion time of wafer lots under the scheduling of the FHRL approach is only about 30 % of the compared methods in the whole SWFS. 相似文献
30.
The kernelized fuzzy c-means algorithm uses kernel methods to improve the clustering performance of the well known fuzzy c-means algorithm by mapping a given dataset into a higher dimensional space non-linearly. Thus, the newly obtained dataset is more likely to be linearly seprable. However, to further improve the clustering performance, an optimization method is required to overcome the drawbacks of the traditional algorithms such as, sensitivity to initialization, trapping into local minima and lack of prior knowledge for optimum paramaters of the kernel functions. In this paper, to overcome these drawbacks, a new clustering method based on kernelized fuzzy c-means algorithm and a recently proposed ant based optimization algorithm, hybrid ant colony optimization for continuous domains, is proposed. The proposed method is applied to a dataset which is obtained from MIT–BIH arrhythmia database. The dataset consists of six types of ECG beats including, Normal Beat (N), Premature Ventricular Contraction (PVC), Fusion of Ventricular and Normal Beat (F), Artrial Premature Beat (A), Right Bundle Branch Block Beat (R) and Fusion of Paced and Normal Beat (f). Four time domain features are extracted for each beat type and training and test sets are formed. After several experiments it is observed that the proposed method outperforms the traditional fuzzy c-means and kernelized fuzzy c-means algorithms. 相似文献