基于时间尖峰序列反向传播方法的事件驱动机器人触觉数据学习

Qing Hou, Tingqing Liu, Jing Yang, Xiaoyang Ji, Qinglang Li, Jian Li, Baofan Yin
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引用次数: 0

摘要

触觉感知是智能机器人像人类一样智能交互的必要条件。因此,有效利用深度学习方法获取触觉特征已成为触觉感知研究的重要焦点。脉冲神经网络具有良好的时间驱动特性和高效处理时空信息的能力,有利于处理基于事件的数据。我们采用一种可以处理连续尖峰的时间尖峰序列学习反向传播方法来改进基于事件驱动数据的触觉物体识别尖峰神经网络。我们在实际应用中证明了时间尖峰序列误差反向传播方法的有效性,该方法利用近似导数解决了数据丢失时间信息的问题。在实际应用中,我们证明了时间尖峰序列误差的反向传播方法在利用近似导数解决数据时间信息丢失问题中的有效性
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Event-driven Robotic Tactile Data Learning Using Temporal Spike Sequence Backpropagation Method
Tactile perception is indispensable for intelligent robots to interact intelligently like humans. Therefore, the effective use of deep learning methods to acquire tactile features has become an important focus of tactile perception research. Satisfactory time-driven characteristics and the ability to process spatiotemporal information efficiently of spiking neural networks are advantageous for event-based data. We apply a temporal spike sequence learning backpropagation method that can handle continuous spikes to improve the spike neural network for tactile object recognition based on event-driven data. We prove the effectiveness of the temporal spike sequence error backpropagation method in practical applications to address the problem of losing temporal information of data using approximate derivatives. In practical application, we have proved the validity of the back propagation method of temporal spike sequence error in solving the problem of losing temporal information of data using approximate derivatives
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