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A Fuzzy-Neural Networks Approach for Multisensor Fusion

机译:多传感器融合的模糊神经网络方法

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There are limit in the multisensor fusion with the conventional fusion methods. Artificial intelligence fusion methods, such as fuzzy logic and neural networks, have received more attention in the recent years. By combining the advantages of fuzzy logic and neural network theory, a fuzzy neural network approach is proposed. Using fuzzy reference describes the states of sensors can avoid sensors' faults or failures. Then reasoning results are taken as inputs of the neural network. The approach can decrease the number of parameters to be trained and increase training efficiency and reduce the complexity. This paper presents the architecture of multisensor fusion based on Fuzzy-Neural network and describes and discusses data fusion algorithm in detail.
机译:具有传统融合方法的多传感器融合中有极限。人工智能融合方法,如模糊逻辑和神经网络,近年来受到更多关注。通过结合模糊逻辑和神经网络理论的优点,提出了一种模糊神经网络方法。使用模糊参考描述了传感器状态可以避免传感器的故障或故障。然后推理结果被视为神经网络的输入。该方法可以减少要训练的参数数量并提高培训效率并降低复杂性。本文介绍了基于模糊神经网络的多传感器融合体系结构,详细描述了数据融合算法。

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