Sine waves frequency identification system modeling based on artificial network operation

Dmitrii D. Piotrovskii, Alexander Kukolev, S. Podgornyi
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Abstract

Sine wave contribution can be observed in many casual periodic processes- starting with nature and finishing with complex hand-made processes like social, economic, technical and biological. This sphere of science have been staying under strict society attention thus having promoted and developed different theories, based on discrete Fourier transform, least squares methods and so on. Technical problem in question can be represented by the list of different processes of wave nature, e.g. sound and light occurrence, wave motion of different mediums. One of the most actual problems in question examples is marine sine wave impact identification for the marine ship main engine speed of rotation adjustment– the process, where control object inevitably is subject to load impact fluctuations. Especially evident this object can be concerned for the Northern Sea Route area, where climate severity is next to the states freights turnover increase desire. In this case marine main engine speed of rotation adjustment without specific control algorithm can be considered to be ineffective because of efficiency drops, increased parts and facilities run-outs. That is why, due to neural networks integration trend into industry processes, we tried to attempt building a separate neural network for defining the frequency of a noisy low-frequency sine wave. The obtained results [1] proved sine waves frequency identification possibility with the help of artificial network, however accuracy was found to be unacceptable because of sketchy algorithm elaboration and small learning array size.
基于人工网络操作的正弦波频率识别系统建模
正弦波的贡献可以在许多偶然的周期性过程中观察到——从自然开始,以复杂的手工过程(如社会、经济、技术和生物)结束。这一科学领域一直受到严格的社会关注,因此促进和发展了不同的理论,基于离散傅里叶变换,最小二乘法等。所讨论的技术问题可以用波动性质的不同过程的列表来表示,例如声和光的发生,不同介质的波动。问题实例中最实际的问题之一是船舶正弦波冲击辨识,这是针对船舶主机转速调整的过程,控制对象不可避免地会受到负载冲击波动的影响。特别明显的是,这个对象可以关注北海航线地区,那里的气候严重程度是下一个国家的货运量增加的愿望。在这种情况下,没有特定控制算法的船舶主机转速调整可以认为是无效的,因为效率下降,零件和设备的故障增加。这就是为什么,由于神经网络集成到工业过程的趋势,我们试图尝试建立一个单独的神经网络来定义噪声低频正弦波的频率。得到的结果[1]证明了人工网络识别正弦波频率的可能性,但由于算法阐述粗略,学习阵列规模小,精度难以接受。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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