Data Mining
Y. Rohani; Z. Torabi; S. Kianian
Abstract
Background: Prediction of students' academic performance is essential for systems emphasizing students' greater success. The results can largely lead to increase in the quality of the educating and learning. Through the application of data mining, useful and innovative patterns can be extracted from ...
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Background: Prediction of students' academic performance is essential for systems emphasizing students' greater success. The results can largely lead to increase in the quality of the educating and learning. Through the application of data mining, useful and innovative patterns can be extracted from the educational data. Methods: In this paper, a new metaheuristic algorithm, combination of simulated annealing and genetic algorithms, is proposed for predicting students’ academic performance in educational data mining. Although metaheuristic algorithms are one of the best options for discovering the hidden relationships between data in data science, they do not separately perform well in accurate prediction of students’ academic performance. Therefore, the proposed method integrates the advantages of both genetic and simulated annealing algorithms. The genetic algorithm is applied to explore new solutions, while simulated annealing is used to increase the exploitation power. By using this combination, the proposed algorithm has been able to predict the students’ academic performance with high accuracy. Results: The efficiency of the proposed algorithm is evaluated on five different educational data sets, including two data sets of students of Shahid Rajaee University of Tehran and three online educational data sets. Our experimental results show and accuracy improvement of the proposed algorithm in comparison to the four similar metaheuristic and five popular classification methods respectively.======================================================================================================Copyrights©2020 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.======================================================================================================
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.======================================================================================================Copyrights©2019 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.======================================================================================================
Control
H. Nasiri Soloklo; N. Bigdeli
Abstract
Background and Objectives: In this paper, a predictive functional control based on Laguerre functions is designed for control of an industrial heating furnace. The fractional order model (FOM) of the heating furnace is assumed as the plant model. Methods: For designing the predictive functional controller ...
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Background and Objectives: In this paper, a predictive functional control based on Laguerre functions is designed for control of an industrial heating furnace. The fractional order model (FOM) of the heating furnace is assumed as the plant model. Methods: For designing the predictive functional controller (PFC), a reduced integer order approximation of the fractional order heating furnace model is derived. The order of the reduced integer model is determined based on Hankel singular values of the original system. Coefficients of the reduced integer model are assumed to be unknown. Unknown parameters are then obtained by minimizing a many-objective fitness function including weighted summation of differences of step responses, steady state errors, maximum overshoots as well as magnitude of frequency responses of the original and reduced systems. Routh-Hurwitz criteria are used as stability criteria and added to optimization problem as its constraints. The optimization tool is Genetic algorithm. Results: Advantages of the proposed method are preserving stability and focusing on various important features of both time and frequency responses of system. In addition, it uses a direct order reduction method without the need to intermediated approximations such as Oustaloup approximation. Conclusion: Laguerre-based PFC controller has been evaluated via two scenarios and the obtained results represent the satisfactory performance of the proposed controller.======================================================================================================Copyrights©2019 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.======================================================================================================
