Artificial Intelligence
N. Ghanbari; S. H. Zahiri; H. Shahraki
Abstract
Background and Objectives: In this paper, a new version of the particle swarm optimization (PSO) algorithm using a linear ranking function is proposed for clustering uncertain data. In the proposed Uncertain Particle Swarm Clustering method, called UPSC method, triangular fuzzy numbers (TFNs) are used ...
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Background and Objectives: In this paper, a new version of the particle swarm optimization (PSO) algorithm using a linear ranking function is proposed for clustering uncertain data. In the proposed Uncertain Particle Swarm Clustering method, called UPSC method, triangular fuzzy numbers (TFNs) are used to represent uncertain data. Triangular fuzzy numbers are a good type of fuzzy numbers and have many applications in the real world.Methods: In the UPSC method input data are fuzzy numbers. Therefore, to upgrade the standard version of PSO, calculating the distance between the fuzzy numbers is necessary. For this purpose, a linear ranking function is applied in the fitness function of the PSO algorithm to describe the distance between fuzzy vectors. Results: The performance of the UPSC is tested on six artificial and nine benchmark datasets. The features of these datasets are represented by TFNs.Conclusion: The experimental results on fuzzy artificial datasets show that the proposed clustering method (UPSC) can cluster fuzzy datasets like or superior to other standard uncertain data clustering methods such as Uncertain K-Means Clustering (UK-means) and Uncertain K-Medoids Clustering (UK-medoids) algorithms. Also, the experimental results on fuzzy benchmark datasets demonstrate that in all datasets except Libras, the UPSC method provides better results in accuracy when compared to other methods. For example, in iris data, the clustering accuracy has increased by 2.67% compared to the UK-means method. In the case of wine data, the accuracy increased with the UPSC method is 1.69%. As another example, it can be said that the increase in accuracy for abalone data was 4%. Comparing the results with the rand index (RI) also shows the superiority of the proposed clustering method.
Artificial Intelligence
I. Behravan; S. M. Razavi
Abstract
Background and Objectives: Stock price prediction has become one of the interesting and also challenging topics for researchers in the past few years. Due to the non-linear nature of the time-series data of the stock prices, mathematical modeling approaches usually fail to yield acceptable results. Therefore, ...
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Background and Objectives: Stock price prediction has become one of the interesting and also challenging topics for researchers in the past few years. Due to the non-linear nature of the time-series data of the stock prices, mathematical modeling approaches usually fail to yield acceptable results. Therefore, machine learning methods can be a promising solution to this problem.Methods: In this paper, a novel machine learning approach, which works in two phases, is introduced to predict the price of a stock in the next day based on the information extracted from the past 26 days. In the first phase of the method, an automatic clustering algorithm clusters the data points into different clusters, and in the second phase a hybrid regression model, which is a combination of particle swarm optimization and support vector regression, is trained for each cluster. In this hybrid method, particle swarm optimization algorithm is used for parameter tuning and feature selection. Results: The accuracy of the proposed method has been measured by 5 companies’ datasets, which are active in the Tehran Stock Exchange market, through 5 different metrics. On average, the proposed method has shown 82.6% accuracy in predicting stock price in 1-day ahead.Conclusion: The achieved results demonstrate the capability of the method in detecting the sudden jumps in the price of a stock.
Artificial Intelligence
F. Jamshidi; M. Vaghefi
Abstract
Background and Objectives: A robot arm is a multi-input multi-output and non-linear system that has many industrial applications. Parameter uncertainties and external disturbances attenuate the performance of this system and a controller design is hence necessary to overcome them.Methods: In ...
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Background and Objectives: A robot arm is a multi-input multi-output and non-linear system that has many industrial applications. Parameter uncertainties and external disturbances attenuate the performance of this system and a controller design is hence necessary to overcome them.Methods: In this paper, the interval Type II Fuzzy fractional-order proportional integral differential (IT2FO-FPID) controller is designed to control a robot arm with 2 degrees of freedom (two-link robot arm). Whale optimization algorithm (WOA) is used to determine the optimal value of controller parameters. The performance of IT2FO-FPID is compared with PID, fractional-order PID (FOPID) and Fuzzy FOPID whose parameters are determined by WOA. The performance of IT2FO-FPID whose parameters are determined by WOA, genetic algorithm, and particle swarm optimization methods are compared.Results: Quantitative and qualitative results of simulations indicate performance improvement with the IT2FO-FPID controller. The ability of WOA in optimizing the parameters of the IT2FO-FPID controller is demonstrated.Conclusion: Sensitivity analysis and the study of the effect of parameter variations and disturbances confirm the robust performance of WOA-based IT2FO-FPID.
Data Mining
I. Behravan; S.H. Zahiri; S.M. Razavi; R. Trasarti
Abstract
Background and Objectives: Big data referred to huge datasets with high number of objects and high number of dimensions. Mining and extracting big datasets is beyond the capability of conventional data mining algorithms including clustering algorithms, classification algorithms, feature selection methods ...
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Background and Objectives: Big data referred to huge datasets with high number of objects and high number of dimensions. Mining and extracting big datasets is beyond the capability of conventional data mining algorithms including clustering algorithms, classification algorithms, feature selection methods and etc. Methods: Clustering, which is the process of dividing the data points of a dataset into different groups (clusters) based on their similarities and dissimilarities, is an unsupervised learning method which discovers useful information and hidden patterns from raw data. In this research a new clustering method for big datasets is introduced based on Particle Swarm Optimization (PSO) algorithm. The proposed method is a two-stage algorithm which first searches the solution space for proper number of clusters and then searches to find the position of the centroids. Results: the performance of the proposed method is evaluated on 13 synthetic datasets. Also its performance is compared to X-means through calculating two evaluation metrics: Rand index and NMI index. The results demonstrate the superiority of the proposed method over X-means for all of the synthetic datasets. Furthermore, a biological microarray dataset is used to evaluate the proposed method deeper. Finally, 2 real big mobility datasets, including the trajectories traveled by several cars in the city of Pisa, are analyzed using the proposed clustering method. The first dataset includes the trajectories recorded in Sunday and the second one contains the trajectories recorded in Monday during 5 weeks. The achieved results showed that people choose more diverse destinations in Sunday although it has fewer trajectories. Conclusion: Finding the number of clusters is a big challenge especially fir big datasets. The results achieved for the proposed method showed its fabulous performance in detecting the number of clusters for high dimensional and massive datasets. Also, the results demonstrate the power and effectiveness of the swarm intelligence methods in solving hard and complex optimization problems.======================================================================================================Copyrights©2018 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.======================================================================================================
