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Research on the Multi-sensors Information Fusion Technique Based on the Neural Networks and its Application

机译:基于神经网络的多传感器信息融合技术研究与应用

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Multi-sensors Information Fusion (MSIF) technology, is being widely applied to various fields, particularly, the modern military field. In order to enhance the capability of ship chemical defense support in the future informationalization sea warfare, a review of the study and application about MSIF technology in Naval Ships Chemical Detection (NSCD) field is researched, the model of NSCD system based on MSIF is built. Its measure system based on multi-sensors fusion could catch the wide information of the chemical agents, carry the feature extraction and selection of the chemical agents through wavelet analysis, then make the best of the Neural Networks to manage the data from multi-sensors system, thus the Neural Networks Distinguishing Chemical Agents (NNDCA) model is built. The realization idea of the NNDCA system is put forward, and the hardware accomplishment and the software structure of the NNDCA system are discussed. The experimental and emulational results show that: it is entirely feasible that using the NNDCA model put up the qualitative and quantitative analysis of the chemical agents; the NNDCA model is capable of, in a great measure, playing down the impact factor of the disturber, concentration and condition, and so on, to measure the chemical agents, as a result, remarkably, the veracity and creditability of measure effect is heightened.
机译:多传感器信息融合(MSIF)技术已广泛应用于各个领域,特别是现代军事领域。为了增强未来信息化海战中船舶化学防御保障的能力,研究了MSIF技术在舰船化学检测领域的研究与应用,建立了基于MSIF的NSCD系统模型。 。其基于多传感器融合的测量系统可以捕获化学试剂的广泛信息,通过小波分析进行化学试剂的特征提取和选择,然后充分利用神经网络来管理来自多传感器系统的数据,因此建立了神经网络区分化学剂(NNDCA)模型。提出了NNDCA系统的实现思想,讨论了NNDCA系统的硬件实现和软件结构。实验和仿真结果表明:使用NNDCA模型对化学试剂进行定性和定量分析是完全可行的; NNDCA模型能够很大程度地降低干扰因素,浓度和条件等的影响因素,从而对化学制剂进行测量,结果,显着提高了测量效果的准确性和可信度。

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