Time series prediction using neural networks and its application to artificial human walking

摘要

One of the main issues in the research on time series is its prediction. Using a tapped-delay neural network we formulate the optimal network size from the signal correlation time. Then the biofeedback-driven neurocontrol for artificial human walking is developed with much detail on the signal preprocessing. Finally we indicate the need for a hierarchical netwrok architecture to elminate oscillatory effects inherent to handling the human motor control problem.