首页> 外文会议>2013 IEEE International Workshop on Genetic and Evolutionary Fuzzy Systems >Estimation of human transport modes by fuzzy spiking neural network and evolution strategy in informationally structured space
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Estimation of human transport modes by fuzzy spiking neural network and evolution strategy in informationally structured space

机译:信息结构化空间中基于模糊尖峰神经网络的人类运输方式估计及其演化策略

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This paper analyzes the performance of human transport mode estimation by fuzzy spiking neural network in informationally structured space based on smart phone sensor. The importance of information structuralization is considered. In our previous work we applied spiking neural network to extract the human position in a room equipped with sensor network devices. In this paper fuzzy spiking neural network is applied to extract the human activity outdoors when equipped with smart phone sensor. We discuss how to update the base value by preprocessing for generating the input values to the spiking neurons. The learning method of the spiking neural network based on the time series of the measured data is explained as well. Evolution strategy is used for optimizing the parameters of the fuzzy spiking neural network. Several experimental results are presented for confirming the effectiveness of the proposed method.
机译:本文分析了基于智能手机传感器的信息结构空间中的模糊尖峰神经网络在人类交通模式估计中的性能。考虑了信息结构化的重要性。在我们以前的工作中,我们应用了尖峰神经网络来提取配备传感器网络设备的房间中的人体位置。本文将模糊加标神经网络应用于装备有智能手机传感器的户外活动。我们讨论了如何通过预处理来更新基本值,以生成尖峰神经元的输入值。阐述了基于实测数据时间序列的尖峰神经网络学习方法。进化策略用于优化模糊尖峰神经网络的参数。提出了几个实验结果,以确认该方法的有效性。

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