جستجوی مقالات مرتبط با کلیدواژه « Chaotic » در نشریات گروه « برق »

Data clustering is a popular analysis tool for data statistics in several fields, including includes pattern recognition, data mining, machine learning, image analysis and bioinformatics, in which the information to be analyzed can be of any distribution in size and shape. Clustering is effective as a technique for discerning the structure of and unraveling the complex relationship between massive amounts of data. See-See partridge chick’s optimization (SSPCO) algorithm is a new optimization algorithm that is inspired by the behavior of a type of bird called see-see partridge. We propose chaotic map SSPCO optimization method for clustering, which uses a chaotic map to adopt a random sequence with a random starting point as a parameter, the method relies on this parameter to update the positions and velocities of the chicks. In the study, twelve different clustering algorithms were extensively compared on thirteen test data sets. The results indicate that the performance of the Chaotic SSPCO method is significantly better than the performance of other algorithms for data clustering problems.

Abstract In this paper, some considerations regarding a ground vehicle oscillating system based on chaotic behaviors are studied. The vehicle system is modeled as a full nonlinear seven degrees of freedom with an additional degree of freedom for each passenger. Roughness of the road surface is considered as sinusoidal waveforms with a time delay for the tires. The governing differential equations are extracted under Newton-Euler laws and are solved via numerical methods. The dynamic behavior of the system is investigated by special nonlinear techniques such as bifurcation diagram, time series, phase plane portrait, power spectrum, Poincaré section, and maximum Lyapunov exponents. The time delays between the tiresare used as a control parameter.First, the vehicle behavior is investigated and the chaotic regions are detected. Then, the damping and stiffness coefficients are used to return to the regular behavior.Results show that by changing the system parameters and selecting the appropriate values one can minimize vibrations, as well as eliminating chaotic behavior. The comparison of the results obtained from the proposed model and those from the vehicle without passengers show the great differences in the dynamic behaviors of two models.

Assigning a set of objects to groups such that objects in one group or cluster are more similar to each other than the other clusters’ objects is the main task of clustering analysis. SSPCO optimization algorithm is a new optimization algorithm that is inspired by the behavior of a type of bird called see see partridge. One of the things that smart algorithms are applied to solve is the problem of clustering. Clustering is employed as a powerful tool in many data mining applications, data analysis, and data compression in order to group data on the number of clusters (groups). In the present article, a chaotic SSPCO algorithm is utilized for clustering data on different benchmarks and datasets; moreover, clustering with artificial bee colony algorithm and particle mass 9 clustering technique is compared. Clustering tests have been done on 13 datasets from UCI machine learning repository. The results show that clustering SSPCO algorithm is a clustering technique which is very efficient in clustering multivariate data.