Current Issue : January - March Volume : 2016 Issue Number : 1 Articles : 5 Articles
This paper presents a hybrid algorithm that combines Fuzzy Logic Controller (FLC) and Genetic Algorithms (GAs) and its application\non a traffic signal system. FLCs have been widely used in many applications in diverse areas, such as control system, pattern\nrecognition, signal processing, and forecasting. They are, essentially, rule-based systems, in which the definition of these rules and\nfuzzy membership functions is generally based on verbally formulated rules that overlap through the parameter space.They have\na great influence over the performance of the system. On the other hand, the Genetic Algorithm is a metaheuristic that provides a\nrobust search in complex spaces. In this work, it has been used to adapt the decision rules of FLCs that define an intelligent traffic\nsignal system, obtaining a higher performance than a classical FLC-based control. The simulation results yielded by the hybrid\nalgorithm show an improvement of up to 34% in the performance with respect to a standard traffic signal controller, Conventional\nTraffic Signal Controller (CTC), and up to 31% in the comparison with a traditional logic controller, FLC....
As the cloud computing develops rapidly, more and more cloud services appear. Many enterprises tend to utilize cloud service\nto achieve better flexibility and react faster to market demands. In the cloud service selection, several experts may be invited\nand many attributes (indicators or goals) should be considered. Therefore, the cloud service selection can be regarded as a kind\nof Multiattribute Group Decision Making (MAGDM) problems. This paper develops a new method for solving such MAGDM\nproblems. In this method, the ratings of the alternatives on attributes in individual decision matrices given by each expert are in\nthe form of interval-valued intuitionistic fuzzy sets (IVIFSs) which can flexibly describe the preferences of experts on qualitative\nattributes. First, the weights of experts on each attribute are determined by extending the classical gray relational analysis (GRA)\ninto IVIF environment. Then, based on the collective decision matrix obtained by aggregating the individual matrices, the score\n(profit) matrix, accuracy matrix, and uncertainty (risk) matrix are derived. A multiobjective programming model is constructed\nto determine the attribute weights. Subsequently, the alternatives are ranked by employing the overall scores and uncertainties\nof alternatives. Finally, a cloud service selection problem is provided to illustrate the feasibility and effectiveness of the proposed\nmethods....
The ability of a fuzzy logic classifier to dynamically\nidentify non-meteorological radar echoes is demonstrated\nusing data from the National Centre for Atmospheric\nScience dual polarisation, Doppler, X-band mobile radar.\nDynamic filtering of radar echoes is required due to the variable\npresence of spurious targets, which can include insects,\nground clutter and background noise. The fuzzy logic classifier\ndescribed here uses novel multi-vertex membership functions\nwhich allow a range of distributions to be incorporated\ninto the final decision. These membership functions are derived\nusing empirical observations, from a subset of the available\nradar data. The classifier incorporates a threshold of certainty\n(25% of the total possible membership score) into the\nfinal fractional defuzzification to improve the reliability of\nthe results. It is shown that the addition of linear texture\nfields, specifically the texture of the cross-correlation coefficient,\ndifferential phase shift and differential reflectivity, to\nthe classifier along with standard dual polarisation radar moments\nenhances the ability of the fuzzy classifier to identify\nmultiple features. Examples from the Convective Precipitation\nExperiment (COPE) show the ability of the filter to identify\ninsects (18 August 2013) and ground clutter in the presence\nof precipitation (17 August 2013). Medium-duration\nrainfall accumulations across the whole of the COPE campaign\nshow the benefit of applying the filter prior to making\nquantitative precipitation estimates. A second deployment at\na second field site (Burn Airfield, 6 October 2014) shows the\napplicability of the method to multiple locations, with small\necho features, including power lines and cooling towers, being\nsuccessfully identified by the classifier without modification\nof the membership functions from the previous deployment.\nThe fuzzy logic filter described can also be run in near\nreal time, with a delay of less than 1 min, allowing its use on\nfuture field campaigns....
This study proposes a fuzzy system for tracking the maximum power point of a PV system for solar\npanel. The solar panel and maximum power point tracker have been modeled using MATLAB/Simulink.\nA simulation model consists of PV panel, boost converter, and maximum power point tack\nMPPT algorithm is developed. Three different conditions are simulated: 1) Uniform irradiation; 2)\nSudden changing; 3) Partial shading. Results showed that fuzzy controller successfully find MPP\nfor all different weather conditions studied. FLC has excellent ability to track MPP in less than 0.01\nsecond when PV is subjected to sudden changes and partial shading in irradiation....
Tsallis entropy is a...
Loading....