Connectionist Learning Algorithms for Temporal Processing and Multi-Scale Search

这笔拨款将支持威廉姆斯博士在人工神经计算的两个重要领域的理论和实验工作：处理时变信号的递归网络，以及在多分辨率下利用信息的多尺度网络。递归网络对于具有未知持续时间反馈延迟的控制和信号处理问题将是重要的。对于具有实际重要性的大规模问题，包括那些具有复杂时空成分的问题，需要多尺度架构和学习策略。提出的目标应用是语音识别和自适应感觉运动控制。

This grant will support Dr. Williams' theoretical and experimental work in two important areas of artificial neural computation: recursive networks for processing time-varying signals, and multiscale networks for exploiting information at multiple resolutions. Recursive networks will be important for control and signal-processing problems with feedback delays of unknown duration. Multiscale architectures and learning strategies are needed for large-scale problems of practical importance, including those with complex spatial and temporal components. Proposed target applications are in speech recognition and adaptive sensorimotor control.