两类模糊系统作为通用逼近器的充分条件的比较与分析

对现有的Mamdani模糊系统和T-S模糊系统作为通用逼近器的充分条件作比较分析,提出了通过三种不同算法获得的单输入单输出Mamdani模糊系统作为通用逼近器的充分条件的保守性比较定理。     

考虑噪声烦恼度的机场飞机噪声收费模型和应用研究

随着我国民航运输业的飞速发展,机场噪声影响问题日趋严重。通过向航空公司或旅客收取噪声费来进行治理,是民航发达国家的普遍做法,也是我国未来的发展方向。该文在国外机场飞机噪声收费模型的基础上,提出了基于飞机噪声特性、噪声烦恼度、噪声治理投入费用和资金回收周期等因素的机场噪声收费模型。模型将机场噪声治理费用通过合理的回收周期按一定比例分配到各年度,再根据飞机噪声特性、噪声烦恼度将每年噪声收费分配到每架次飞机(或每位旅客)。飞机噪声特性根据ACI的机场噪声分级R1～R8来确定,噪声烦恼度借助Mamdani模型构建。应用表明,该收费模型关键因素考虑全面,计算方法先进简洁,费率合理。对于噪声较高飞机,夜间起飞收费为1219.06元/架次,客均收费10.16元;对于噪声很低的飞机,昼间着陆收费为66.10元/架次,客均收费仅0.15元。费率确定符合国际民航组织倡导的以支定收原则,有助于形成航空公司/旅客减噪激励机制同时又不致抑制机场航空业务增长。     

基于三角模糊集划分的MISO Mamdani模糊系统参数关联性分析

基于三角模糊集划分的MISO Mamdani模糊系统是模糊规则获取研究领域的一类重点研究对象。在这种划分下，系统的输入空间被划分成若干个超立方体，在每个超立方体内系统的输出与2^p条模糊规则中的参数相关。本文将每个超立方体分成若干个区域，并且严格论证了在每个区域内系统输出仅与其中2^p-1条模糊规则及另2^p-1条模糊规则中相应参数平均值相关。这个结论使得可以根据样本误差的区域分布情况优化参数的选取。对于该类系统的参数优化过程中的参数选取具有积极的意义。     

Learning concurrently partition granularities and rule bases of Mamdani fuzzy systems in a multi-objective evolutionary framework

In this paper we propose a multi-objective evolutionary algorithm to generate Mamdani fuzzy rule-based systems with different good trade-offs between complexity and accuracy. The main novelty of the algorithm is that both rule base and granularity of the uniform partitions defined on the input and output variables are learned concurrently. To this aim, we introduce the concepts of virtual and concrete rule bases: the former is defined on linguistic variables, all partitioned with a fixed maximum number of fuzzy sets, while the latter takes into account, for each variable, a number of fuzzy sets as determined by the specific partition granularity of that variable. We exploit a chromosome composed of two parts, which codify the variables partition granularities, and the virtual rule base, respectively. Genetic operators manage virtual rule bases, whereas fitness evaluation relies on an appropriate mapping strategy between virtual and concrete rule bases. The algorithm has been tested on two real-world regression problems showing very promising results.     

基于模糊推理的纹理图像融合方法研究

研究了模糊逻辑方法在图像特征层融合中的应用,介绍了两种模糊推理方法—Mamdani型和Sugeno型模糊逻辑推理系统,以及基于Sugeno型模糊推理的自适应模糊神经网络系统;在红外-可见光双通道图像融合实验中,采用Gabor滤波提取两幅图像的纹理特征,并分别采用两种模糊推理方法进行特征层融合。对融合效果进行了分析,结果表明两种模糊逻辑方法在不同应用环境下都可以取得了较好的融合效果。     

Predicting soil fragmentation during tillage operation using fuzzy logic approach

One of the main characteristics of the soil structure, which affects the plant growth and its yield, is its aggregates size. Correct tillage operations leads to prevention from soil degradation and help to maintain and improve its physical, chemical, and biological characteristics. In this paper, a model based on fuzzy logic approach was used to describe the soil fragmentation for seedbed preparation in the composition of primary and secondary tillage implements of subsoiler, moldboard plow and disk harrow as conventional tillage composition in the region. Field experiments were carried out at educational and research farms of faculty of agriculture, University of Mohaghegh Ardabili. In this paper, an intelligent model, based on Mamdani approach fuzzy modeling principles, was developed to predict soil fragmentation during tillage operation. The model inputs included soil moisture content, tractor forward speed and soil sampling depth. The fuzzy model consisted of 50 rules, in which three parameters of root mean square error (RMSE), relative error (ɛ), and coefficient of determination (R²) were used to evaluate the fuzzy model. These parameters were calculated 0.167%, 3.95%, and 0.988%, respectively. According to the results of this research, the fuzzy model can be introduced as one of the methods for predicting soil fragmentation during the tillage operation with high accuracy.