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A distributed robotic control system based on a temporal self-organizing neural network

机译:基于时间自组织神经网络的分布式机器人控制系统

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A distributed robot control system is proposed based on a temporal self-organizing neural network, called competitive and temporal Hebbian (CTH) network. The CTH network can learn and recall complex trajectories by means of two sets of synaptic weights, namely, competitive feedforward weights that encode the individual states of the trajectory and Hebbian lateral weights that encode the temporal order of trajectory states. Complex trajectories contain repeated or shared states which are responsible for ambiguities that occur during trajectory reproduction. Temporal context information are used to resolve such uncertainties. Furthermore, the CTH network saves memory space by maintaining only a single copy of each repeated/shared state of a trajectory and a redundancy mechanism improves the robustness of the network against noise and faults. The distributed control scheme is evaluated in point-to-point trajectory control tasks using a PUMA 560 robot. The performance of the control system is discussed and compared with other unsupervised and supervised neural network approaches. We also discuss the issues of stability and convergence of feedforward and lateral learning schemes.
机译:提出了一种基于时间自组织神经网络的分布式机器人控制系统,称为竞争性和时间性的Hebbian(CTH)网络。 CTH网络可以通过两组突触权重来学习和调用复杂的轨迹,即,竞争性前馈权重(对轨迹的各个状态进行编码)和赫比侧向权重(对轨迹状态的时间顺序进行编码)。复杂轨迹包含重复或共享状态,这些状态导致轨迹再现过程中出现歧义。时间上下文信息用于解决此类不确定性。此外,CTH网络通过仅维护轨迹的每个重复/共享状态的单个副本来节省内存空间,并且冗余机制提高了网络抗噪声和故障的鲁棒性。使用PUMA 560机器人在点对点轨迹控制任务中评估分布式控制方案。讨论了控制系统的性能,并将其与其他无监督和受监督的神经网络方法进行了比较。我们还将讨论前馈和横向学习方案的稳定性和收敛性问题。

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