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Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition 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 Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition 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.)

To improve the prediction accuracy of wind power， a parameter self-tuning model is proposed. The sliding window weighted recursive least square (SWWRLS) method is used to establish the wind power prediction model， which emphasizes most recent data in the prediction， ruling out the interference of historical data. Less memory space is used in the weighted recursive model， and the adaptability of the prediction model to the external environment data is increased. Finally， the simulation is carried out on the real historical data of the wind farm in Liaoning with support vector machine， Kalman filter and SWWRLS methods， separately， and the simulation results show the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

To improve the optimization performance of harmony search algorithm， an improved novel global harmony search (INGHS) algorithm was proposed. The harmony memory diversity was defined by the difference vector norm to implement dynamically position updating， and the mutation operation was employed to update harmony memory. A new harmony generated with dynamically updating strategy made the algorithm having excellent global search performance in the early states and local search capability during the final stages of the optimization process， which avoided the algorithm trapped in local optimal. Finally， the simulation for 7 benchmark functions were carried out using the proposed algorithm and other state-of-the-art HS variants that reported in the recent literature. The simulation results demonstrated that the proposed algorithm has a higher precision. [ABSTRACT FROM AUTHOR]