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提出了一种基于遗传算法与多层感知神经网络的调制识别方法,运用改进遗传算法优化的多层感知神经网络分类器对各种调制信号的特征矢量进行分类识别.利用遗传算法的高效全局特性,克服了传统BP算法易于陷入局部最优解的缺点,同时在遗传算法基础上增加梯度下降算子,加快了收敛速度,使得分类器的识别率、收敛速度和鲁棒性得到明显改善,仿真实验的结果证明了此方法的有效性和可行性. 相似文献
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The extraction of stable local features directly affects the performance of infrared face recognition algorithms.Recent studies on the application of scale invariant feature transform(SIFT) to infrared face recognition show that star-styled window filter(SWF) can filter out errors incorrectly introduced by SIFT.The current letter proposes an improved filter pattern called Y-styled window filter(YWF) to further eliminate the wrong matches.Compared with SWF,YWF patterns are sparser and do not maintain rotation invariance;thus,they are more suitable to infrared face recognition.Our experimental results demonstrate that a YWF-based averaging window outperforms an SWF-based one in reducing wrong matches,therefore improving the reliability of infrared face recognition systems. 相似文献
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We present a novel system for parameter design and optimization of modulated lidar. The system is realized by combining software simulation with hardware circuit. This method is more reliable and flexible for lidar parameter optimization compared with theoretical computation or fiber-simulated system. Experiments confirm that the system is capable of optimizing parameters for modulated lidar. Key parameters are analyzed as well. The optimal filter bandwidth is 200 MHz and the optimal modulation depth is 0.5 under typical application environment. 相似文献
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