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<table border="0" cellpadding="3" cellspacing="1" width="100%" class="forumline"> <tr> <th class="thCornerL" width="22%" height="26">Autor</th> <th class="thCornerR">Wiadomość</th> </tr> <tr> <td width="22%" align="left" valign="top" class="row1"><span class="name"><a name=""></a><b>loiwtjsf</b></span></td> <td class="row1" height="28" valign="top"><table width="100%" border="0" cellspacing="0" cellpadding="0"> <tr> <td width="100%"><img src="http://picsrv.fora.pl/subSilver/images/icon_minipost.gif" width="12" height="9" alt="Post" title="Post" border="0" /><span class="postdetails">Wysłany: Nie 2:09, 12 Gru 2010<span class="gen"> </span> Temat postu: tory burch shoes An adaptive genetic algorithm usi</span></td> </tr> <tr> <td colspan="2"><hr /></td> </tr> <tr> <td colspan="2"><span class="postbody">An adaptive genetic algorithm using fuzzy self-tuning controller <br /> <br /> <br />32. The simulation results shown in Figure 3. (A) step response (b) the objective function in Figure 3 the general genetic algorithm using fuzzy controller step response curve with the objective function when other parameters constant, P and P using adaptive algorithm, get objective function value ' , = 25.1515. The simulation results shown in Figure 4. Figure 4,<a href="http://www.toryburchsaler.net" target="_blank" class="postlink">tory burch shoes</a>, the genetic algorithm with adaptive step response of fuzzy controller with the objective function curves:: Naval University of Engineering Chapter 19 under the same conditions as in Figure 5, the general fuzzy self-tuning controller step response curve. Figures 3 and 4,<a href="http://www.stivaliuggboots.com" target="_blank" class="postlink">ugg milano</a>, the contrast can be seen that the genetic algorithm using adaptive fuzzy controller than the controller does not use this program on the rise time has been shortened,<a href="http://www.stivaliuggboots.com" target="_blank" class="postlink">ugg stivali</a>, the objective function value of the smaller, more close to the global optimal solution; to Figure 3,4 and 5,<a href="http://www.toryburchsaler.net" target="_blank" class="postlink">tory burch sale</a>, contrast can be seen, the use of adaptive genetic algorithm program can greatly accelerate the convergence rate of the controller,<a href="http://www.uggbootsgunstigbillig.com" target="_blank" class="postlink">ugg boots günstig</a>, and the overshoot can be well suppressed, is superior to the general fuzzy self-tuning controller. 3 Conclusion ugly solving a <scale t / S Figure 5 General Fuzzy self-tuning controller step response curves of this paper, an adaptive fuzzy controller for adaptive genetic algorithms, adaptive fuzzy controller to improve the traditional The parameter tuning method is designed based on the adaptive genetic algorithm based fuzzy self-tuning controller, and this new controller and more mature self-tuning fuzzy controller comparative study of simulation. The results show that the adaptive genetic algorithm, the controller performance is greatly improved. References: [1] I () ETAM0NPH0NGJ, FANGSC, YoungR. Multi-objectiveoptimizationproblemswithfuzzyrelationequationconstraints [J]. FuzzySetsandSystems, 2002,2 (4) :141-164. [2] TESSEMAE, GIRMAB. ASelf-adaptiveGeneticAlgorithmforConstrainedOptimization [D]. America: Oklaho-m3StateUniversity, 2007. [3] SAIFUDDINGMD, TAREEQEP, PARVEENR. Robustfacedetectionusinggeneticalgorithm [J]. Informa-tionTechnologyJournal, 2007,6 (1) :142-147. [4] Wang Xiaoping, Cao Liming. Genetic algorithms - theory, application and software implementation [M]. Xi'an: Xi'an Jiaotong University Press, 2002. [5] Sun Zengqi. Intelligent Control Theory and Technology [M]. Beijing: Tsinghua University Press, 1997. [6] Xu Jiangning, Ma Hang, ROCKETS, et al. Course based on genetic algorithm analysis of measurement errors [J]. Naval University of Engineering, 2004,16 (2); 12-16. [7] high Yongqi, Wang Shi. Kinematics ROBOFISH Test Method for Optimization [J]. Naval University of Engineering, 2006,18 (4); 39-44. (Continued from page 102) 4 Conclusion This paper analyzes the range sidelobe suppression filter for encoding signals, then a coded signal a comprehensive selection methods, a high speed moving target detection performance has been greatly enhanced. If on this basis with the local oscillator compensation, neural network optimization, multi-channel processing methods can further improve radar detection of moving targets. References: [1] Xiao-Juan Liu, Xu Yuan. Phase-coded signal in the LPI Radar [J]. Modern Radar, 2003 (7) :11-13. [2] Fu Yaoxian. Phase-coded radar performance and Applications [M]. Nanjing: Nanjing University of Technology, 2002. [3] Wu Jianhui, Jing-Cheng, Li Aifei. Taylor four-phase frequency-domain code sidelobe suppression digital filter design [J]. Signal Processing, 2001,17 (5) :395 - 399. [4] Liu Feng, Wu Guisheng, Huang Shuanghua, et al. Phase encoding sidelobe suppression filter design and performance analysis [C] / / Ninth Annual Conference Proceedings of radar. Yantai: Radio Detection Branch of the Chinese Institute of Electronics, 2004. [5] Wang Wei. Based on genetic algorithm and neural network two-phase code sidelobe suppression [J]. Fire Control Radar Technology, 2002 (6) :43-47. <br /> <br />相关的主题文章： <br /> <br /> <br /> <a href="http://gamesfull.forumup.it/viewtopic.php?p=36468&mforum=gamesfull#36468" target="_blank" class="postlink">ugg boots günstig New generation of portable airc</a> <br /> <br /> <a href="http://www.outofkilter.net/forum/viewtopic.php?p=140554#140554" target="_blank" class="postlink">stivali ugg Weighing the results of alternative Me</a> <br /> <br /> <a href="http://zone.ku6.com/u/9532611/entry/4041295.html" target="_blank" class="postlink">ugg boots billig Frequency measurement using capacitance _51</a></span></td> </tr> </table></td> </tr> <tr> <td colspan="2" height="1" class="spaceRow"><img src="http://picsrv.fora.pl/subSilver/images/spacer.gif" alt="" width="1" height="1" /></td> </tr> </table>
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