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Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of East China University of Science & Technology is the property of Journal of East China University of Science & Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper established a non-convex control model consists of time-invariant nonlinear system,non-affine equality constraint and non-convex inequality constrain aiming at the path planning problem of unmanned aerial vehicle,along with an algorithm designed for solving the aforementioned model.Based on iterative optimization,it proposed the convex optimization iteration method and penalty function optimization strategy.The former used the concave-convex process(CCCP) and Taylor formula to convexity the model,while the latter added the processed term to the objective function as a penalty term to solve the feasibility limit of the initial point.It is proved that the proposed method strictly converges to a Karush-Kuhn-Tucker(KKT)point of the original problem.Simulation experimental results verify the feasibility and superiority of the penalty function convex optimization iteration algorithm,and it indicates that the proposed algorithm can provide a flight path satisfying the conditions for the unmanned aerial vehicle. [ABSTRACT FROM AUTHOR]

The loss function of SVM is able to guarantee the high confidence of classification results, but it is also an unbounded convex function which is greatly affected by noise. In order to improve the classification effect of SVM in noisy environment, this paper proposed ML loss combined with pinball and LS loss functions to reduce the sensitivity to noise, which was applied to SVM to obtain MLSVM model. The algorithm simplified the solution process according to the characteristics of LS loss function with structural risk minimization and equality constraints, then used pinball loss function to determine the classification hyperplanes according to the max quantile distance between classification samples and used Lagrange function and other methods to work out the objective function and classification hyperplanes of MLSVM. Experiments on datasets show that compared with hinge SVM and other models, MLSVM is capable of reducing the sensitivity to noise in data and improving the recognition performance of noise-containing data. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)